{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = 'PCI_BUS_ID'\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0,3,6'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_of_gpu = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40e42b9018a34756b4339f7671dfcdc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm_notebook\n",
    "tqdm_notebook().pandas()\n",
    "import re\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import copy\n",
    "import cv2\n",
    "from os import listdir\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub  #pip install tensorflow_hub\n",
    "import os\n",
    "from tokenization import FullTokenizer\n",
    "# from tqdm import tqdm_notebook\n",
    "from tensorflow.keras import backend as K\n",
    "# from keras import backend as K\n",
    "# Initialize session\n",
    "# sess = tf.Session()\n",
    "sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) #command to run codeon multiple gpu\n",
    "\n",
    "\n",
    "\n",
    "# Params for bert model and tokenization\n",
    "# bert_path = \"https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1\"\n",
    "\n",
    "bert_path = \"https://tfhub.dev/google/bert_chinese_L-12_H-768_A-12/1\"\n",
    "from keras_lr_finder import LRFinder\n",
    "import talos as ta\n",
    "from pprint import pprint\n",
    "from talos import live\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "from random import choice\n",
    "import gc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from talos.model.normalizers import lr_normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.set_random_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_nonrumor.txt  test_rumor.txt  train_nonrumor.txt  train_rumor.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls WeiboRumorSet/tweets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images\t   nonrumor_images  rumor_images\ttweets\r\n",
      "names.txt  readme.txt\t    social_feature.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls WeiboRumorSet/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(file):\n",
    "    return pd.read_csv(file, sep='|',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_fake = get_df('WeiboRumorSet/tweets/train_rumor.txt')\n",
    "train_df_real = get_df('WeiboRumorSet/tweets/train_nonrumor.txt')\n",
    "test_df_fake = get_df('WeiboRumorSet/tweets/test_rumor.txt')\n",
    "test_df_real = get_df('WeiboRumorSet/tweets/test_nonrumor.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fake = train_df_fake[0].tolist()\n",
    "train_real = train_df_real[0].tolist()\n",
    "test_fake = test_df_fake[0].tolist()\n",
    "test_real = test_df_real[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_offset(list_):\n",
    "    fixed_flag = False\n",
    "\n",
    "    while not fixed_flag:\n",
    "        exit_flag=False\n",
    "        temp = copy.deepcopy(list_)\n",
    "        for i,v in enumerate(temp):\n",
    "            if v!=None:\n",
    "                if 'sinaimg.cn' in v:\n",
    "                    if list_[i+1] !=None:\n",
    "                        if list_[i+1].isdigit():\n",
    "                            list_.insert(i+1,None)\n",
    "                            exit_flag=True\n",
    "                            break\n",
    "        if not exit_flag:\n",
    "            fixed_flag=True\n",
    "            \n",
    "    return list_\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fake = fix_offset(train_fake) \n",
    "train_real = fix_offset(train_real)\n",
    "test_fake = fix_offset(test_fake)\n",
    "test_real = fix_offset(test_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def break_in_block(list_):\n",
    "    temp = []\n",
    "    for i in range(0,len(list_),3):\n",
    "        temp.append(list_[i:i+3])\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fake = break_in_block(train_fake)\n",
    "train_real = break_in_block(train_real)\n",
    "test_fake = break_in_block(test_fake)\n",
    "test_real = break_in_block(test_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3748, 3783, 1000, 996)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_fake),len(train_real),len(test_fake),len(test_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_and_text_list(blocks_list):\n",
    "    image_list = []\n",
    "    text_list = []\n",
    "    for i in blocks_list:\n",
    "        if i[-1] !=None:\n",
    "            image_list.append(i[1])\n",
    "            text_list.append(i[-1])\n",
    "    image_list = [i.split('/')[-1] for i in image_list]\n",
    "    return image_list, text_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fake_image,train_fake_text = get_image_and_text_list(train_fake)\n",
    "train_real_image,train_real_text = get_image_and_text_list(train_real)\n",
    "test_fake_image,test_fake_text = get_image_and_text_list(test_fake)\n",
    "test_real_image,test_real_text = get_image_and_text_list(test_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_fake_Y = [0]*len(train_fake_image)\n",
    "train_real_Y = [1]*len(train_real_image)\n",
    "test_fake_Y = [0]*len(test_fake_image)\n",
    "test_real_Y = [1]*len(test_real_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_fake_image+train_real_image\n",
    "train_text = train_fake_text + train_real_text\n",
    "trainY = train_fake_Y+train_real_Y\n",
    "\n",
    "test_images = test_fake_image+test_real_image\n",
    "test_text = test_fake_text+test_real_text\n",
    "testY = test_fake_Y+test_real_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7481, 7481, 7481, 1930, 1930, 1930)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_images),len(train_text),len(trainY),len(test_images),len(test_text),len(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = np.array(train_images)\n",
    "train_text = np.array(train_text)\n",
    "trainY = np.array(trainY)\n",
    "test_images = np.array(test_images)\n",
    "test_text = np.array(test_text)\n",
    "testY = np.array(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def index_to_delete(list_):\n",
    "    list_images_dir = listdir('WeiboRumorSet/images/')\n",
    "    gif_list = ['957e1cf2tw1e5foxts295g206o03p4qp.gif','a716fd45jw1ev0cgf8j46g209505zh4i.gif','005vnhZYgw1evupo8ttddg308w06o4qp.gif','7da75521gw1ele2jvi85rg2096056u0x.gif']\n",
    "    index = []\n",
    "    for i,v in enumerate(list_):\n",
    "        if v not in list_images_dir:\n",
    "            index.append(i)\n",
    "        if v in gif_list:\n",
    "            index.append(i)\n",
    "    return index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4147"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_delete_index =index_to_delete(train_images)\n",
    "test_delete_index = index_to_delete(test_images)\n",
    "len(train_delete_index)+len(test_delete_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = np.delete(train_images,train_delete_index)\n",
    "train_text = np.delete(train_text,train_delete_index)\n",
    "trainY = np.delete(trainY,train_delete_index)\n",
    "test_images = np.delete(test_images,test_delete_index)\n",
    "test_text = np.delete(test_text,test_delete_index)\n",
    "testY = np.delete(testY,test_delete_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffle_index= np.arange(len(train_images))\n",
    "np.random.shuffle(shuffle_index)\n",
    "train_images = train_images[shuffle_index]\n",
    "train_text = train_text[shuffle_index]\n",
    "trainY = trainY[shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3487, 1964, 1582, ..., 3092, 3772,  860])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffle_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4140, 4140, 4140, 1124, 1124, 1124)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_images),len(train_text),len(trainY),len(test_images),len(test_text),len(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the maximum document length\n",
    "def max_length(lines):\n",
    "    return max([len(s) for s in lines])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max len 498\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([5.800e+02, 5.580e+02, 2.404e+03, 5.530e+02, 3.100e+01, 8.000e+00,\n",
       "        4.000e+00, 1.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([  0,  50, 100, 150, 200, 250, 300, 350, 400, 450, 498]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'max len {max_length(train_text)}')\n",
    "plt.hist([len(s) for s in train_text],bins=[0,50,100,150,200,250,300,350,400,450,498])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_seq_length=200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acbfc635fc5f43e9857139933ac437bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Converting examples to features', max=4140, style=ProgressSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d641e43d7ab4b75b2015460f71ac9de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Converting examples to features', max=1124, style=ProgressSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "class PaddingInputExample(object):\n",
    "    \"\"\"Fake example so the num input examples is a multiple of the batch size.\n",
    "  When running eval/predict on the TPU, we need to pad the number of examples\n",
    "  to be a multiple of the batch size, because the TPU requires a fixed batch\n",
    "  size. The alternative is to drop the last batch, which is bad because it means\n",
    "  the entire output data won't be generated.\n",
    "  We use this class instead of `None` because treating `None` as padding\n",
    "  battches could cause silent errors.\n",
    "  \"\"\"\n",
    "\n",
    "class InputExample(object):\n",
    "    \"\"\"A single training/test example for simple sequence classification.\"\"\"\n",
    "\n",
    "    def __init__(self, guid, text_a, text_b=None, label=None):\n",
    "        \"\"\"Constructs a InputExample.\n",
    "    Args:\n",
    "      guid: Unique id for the example.\n",
    "      text_a: string. The untokenized text of the first sequence. For single\n",
    "        sequence tasks, only this sequence must be specified.\n",
    "      text_b: (Optional) string. The untokenized text of the second sequence.\n",
    "        Only must be specified for sequence pair tasks.\n",
    "      label: (Optional) string. The label of the example. This should be\n",
    "        specified for train and dev examples, but not for test examples.\n",
    "    \"\"\"\n",
    "        self.guid = guid\n",
    "        self.text_a = text_a\n",
    "        self.text_b = text_b\n",
    "        self.label = label\n",
    "\n",
    "def create_tokenizer_from_hub_module():\n",
    "    \"\"\"Get the vocab file and casing info from the Hub module.\"\"\"\n",
    "    bert_module =  hub.Module(bert_path)\n",
    "    tokenization_info = bert_module(signature=\"tokenization_info\", as_dict=True)\n",
    "    vocab_file, do_lower_case = sess.run(\n",
    "        [\n",
    "            tokenization_info[\"vocab_file\"],\n",
    "            tokenization_info[\"do_lower_case\"],\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    return FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)\n",
    "\n",
    "def convert_single_example(tokenizer, example, max_seq_length=256):\n",
    "    \"\"\"Converts a single `InputExample` into a single `InputFeatures`.\"\"\"\n",
    "\n",
    "    if isinstance(example, PaddingInputExample):\n",
    "        input_ids = [0] * max_seq_length\n",
    "        input_mask = [0] * max_seq_length\n",
    "        segment_ids = [0] * max_seq_length\n",
    "        label = 0\n",
    "        return input_ids, input_mask, segment_ids, label\n",
    "\n",
    "    tokens_a = tokenizer.tokenize(example.text_a)\n",
    "    if len(tokens_a) > max_seq_length - 2:\n",
    "        tokens_a = tokens_a[0 : (max_seq_length - 2)]\n",
    "\n",
    "    tokens = []\n",
    "    segment_ids = []\n",
    "    tokens.append(\"[CLS]\")\n",
    "    segment_ids.append(0)\n",
    "    for token in tokens_a:\n",
    "        tokens.append(token)\n",
    "        segment_ids.append(0)\n",
    "    tokens.append(\"[SEP]\")\n",
    "    segment_ids.append(0)\n",
    "\n",
    "    input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
    "\n",
    "    # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
    "    # tokens are attended to.\n",
    "    input_mask = [1] * len(input_ids)\n",
    "\n",
    "    # Zero-pad up to the sequence length.\n",
    "    while len(input_ids) < max_seq_length:\n",
    "        input_ids.append(0)\n",
    "        input_mask.append(0)\n",
    "        segment_ids.append(0)\n",
    "\n",
    "    assert len(input_ids) == max_seq_length\n",
    "    assert len(input_mask) == max_seq_length\n",
    "    assert len(segment_ids) == max_seq_length\n",
    "\n",
    "    return input_ids, input_mask, segment_ids, example.label\n",
    "\n",
    "def convert_examples_to_features(tokenizer, examples, max_seq_length=256):\n",
    "    \"\"\"Convert a set of `InputExample`s to a list of `InputFeatures`.\"\"\"\n",
    "\n",
    "    input_ids, input_masks, segment_ids, labels = [], [], [], []\n",
    "    for example in tqdm_notebook(examples, desc=\"Converting examples to features\"):\n",
    "        input_id, input_mask, segment_id, label = convert_single_example(\n",
    "            tokenizer, example, max_seq_length\n",
    "        )\n",
    "        input_ids.append(input_id)\n",
    "        input_masks.append(input_mask)\n",
    "        segment_ids.append(segment_id)\n",
    "        labels.append(label)\n",
    "    return (\n",
    "        np.array(input_ids),\n",
    "        np.array(input_masks),\n",
    "        np.array(segment_ids),\n",
    "        np.array(labels).reshape(-1, 1),\n",
    "    )\n",
    "\n",
    "def convert_text_to_examples(texts, labels):\n",
    "    \"\"\"Create InputExamples\"\"\"\n",
    "    InputExamples = []\n",
    "    for text, label in zip(texts, labels):\n",
    "        InputExamples.append(\n",
    "            InputExample(guid=None, text_a=\" \".join(text), text_b=None, label=label)\n",
    "        )\n",
    "    return InputExamples\n",
    "\n",
    "# Instantiate tokenizer\n",
    "tokenizer = create_tokenizer_from_hub_module()\n",
    "\n",
    "# Convert data to InputExample format\n",
    "train_examples = convert_text_to_examples(train_text, trainY)\n",
    "test_examples = convert_text_to_examples(test_text, testY)\n",
    "\n",
    "# Convert to features\n",
    "(train_input_ids, train_input_masks, train_segment_ids, trainY \n",
    ") = convert_examples_to_features(tokenizer, train_examples, max_seq_length=max_seq_length)\n",
    "(test_input_ids, test_input_masks, test_segment_ids, testY\n",
    ") = convert_examples_to_features(tokenizer, test_examples, max_seq_length=max_seq_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 224\n",
    "width = 224\n",
    "channels = 3\n",
    "+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = []\n",
    "def read_and_process_image(list_of_images):\n",
    "    X = [] \n",
    "    for image in tqdm_notebook(list_of_images):\n",
    "#         print(image)\n",
    "        a = cv2.imread(image, cv2.IMREAD_COLOR)\n",
    "        try:\n",
    "            X.append(cv2.resize(a, (length,width), interpolation=cv2.INTER_CUBIC))  \n",
    "        except:\n",
    "            print(a)\n",
    "            error.append(image)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = ['WeiboRumorSet/images/'+i for i in train_images]\n",
    "test_images = ['WeiboRumorSet/images/'+i for i in test_images]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c0aaacc8b674f879c47af6a6f6d1e43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4140), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aef6ea6774274288b0381f473f21c8d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1124), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_imagesX = read_and_process_image(train_images)\n",
    "test_imagesX = read_and_process_image(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('train_imagesX.npy', train_imagesX)\n",
    "np.save('test_imagesX.npy', test_imagesX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imagesX = np.load('train_imagesX.npy')\n",
    "test_imagesX = np.load('test_imagesX.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imagesX = np.rollaxis(train_imagesX, 3, 1)\n",
    "test_imagesX = np.rollaxis(test_imagesX,3,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BertLayer(tf.layers.Layer):\n",
    "    def __init__(self, n_fine_tune_layers=10, **kwargs):\n",
    "        self.n_fine_tune_layers = n_fine_tune_layers\n",
    "        self.trainable = True\n",
    "        self.output_size = 768\n",
    "        super(BertLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        self.bert = hub.Module(\n",
    "            bert_path,\n",
    "            trainable=self.trainable,\n",
    "            name=\"{}_module\".format(self.name)\n",
    "        )\n",
    "\n",
    "        trainable_vars = self.bert.variables\n",
    "\n",
    "        # Remove unused layers\n",
    "        trainable_vars = [var for var in trainable_vars if not \"/cls/\" in var.name]\n",
    "\n",
    "        # Select how many layers to fine tune\n",
    "        trainable_vars = trainable_vars[-self.n_fine_tune_layers :]\n",
    "\n",
    "        # Add to trainable weights\n",
    "        for var in trainable_vars:\n",
    "            self._trainable_weights.append(var)\n",
    "            \n",
    "        for var in self.bert.variables:\n",
    "            if var not in self._trainable_weights:\n",
    "                self._non_trainable_weights.append(var)\n",
    "\n",
    "        super(BertLayer, self).build(input_shape)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        inputs = [K.cast(x, dtype=\"int32\") for x in inputs]\n",
    "        input_ids, input_mask, segment_ids = inputs\n",
    "        bert_inputs = dict(\n",
    "            input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids\n",
    "        )\n",
    "        result = self.bert(inputs=bert_inputs, signature=\"tokens\", as_dict=True)[\n",
    "            \"pooled_output\"\n",
    "        ]\n",
    "        return result\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return (input_shape[0], self.output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_vars(sess):\n",
    "    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) #command to run codeon multiple gpu\n",
    "    sess.run(tf.local_variables_initializer())\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    sess.run(tf.tables_initializer())\n",
    "    K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def news_model(x_train, y_train, x_val, y_val, params):\n",
    "    \n",
    "    try:\n",
    "        del model\n",
    "    except:\n",
    "        pass\n",
    "    K.clear_session()\n",
    "    gc.collect()\n",
    "    \n",
    "    with tf.device('/cpu:0'):\n",
    "        bert_base = BertLayer()\n",
    "        bert_base.trainable= params['bert_trainable']\n",
    "\n",
    "        in_id = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_ids\")\n",
    "        in_mask = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_masks\")\n",
    "        in_segment = tf.keras.layers.Input(shape=(max_seq_length,), name=\"segment_ids\")\n",
    "        bert_inputs = [in_id, in_mask, in_segment]\n",
    "        bert_output = bert_base(bert_inputs)\n",
    "\n",
    "        if params['text_no_hidden_layer']>0:\n",
    "            for i in range(params['text_no_hidden_layer']):\n",
    "                bert_output = tf.keras.layers.Dense(params['text_hidden_neurons'], activation='relu')(bert_output)\n",
    "                bert_output = tf.keras.layers.Dropout(params['dropout'])(bert_output)\n",
    "\n",
    "        text_repr = tf.keras.layers.Dense(params['repr_size'], activation='relu')(bert_output)\n",
    "\n",
    "        #image model\n",
    "        conv_base = tf.keras.applications.VGG19(weights='imagenet', include_top=False, input_shape=(3,224,224))\n",
    "        conv_base.trainable=False\n",
    "#         conv_base = base\n",
    "\n",
    "        input_image = tf.keras.layers.Input(shape=(3,224,224))\n",
    "        base_output = conv_base(input_image)\n",
    "        flat = tf.keras.layers.Flatten()(base_output)\n",
    "\n",
    "        if params['vis_no_hidden_layer']>0:\n",
    "            for i in range(params['vis_no_hidden_layer']):\n",
    "                flat = tf.keras.layers.Dense(params['vis_hidden_neurons'], activation='relu')(flat)\n",
    "                flat = tf.keras.layers.Dropout(params['dropout'])(flat)\n",
    "\n",
    "        visual_repr = tf.keras.layers.Dense(params['repr_size'],activation='relu')(flat)\n",
    "\n",
    "\n",
    "        #classifier\n",
    "        combine_repr = tf.keras.layers.concatenate([text_repr, visual_repr])\n",
    "        com_drop=tf.keras.layers.Dropout(params['dropout'])(combine_repr)\n",
    "\n",
    "        if params['final_no_hidden_layer']>0:\n",
    "            for i in range(params['final_no_hidden_layer']):\n",
    "                com_drop = tf.keras.layers.Dense(params['final_hidden_neurons'], activation='relu')(com_drop)\n",
    "                com_drop=tf.keras.layers.Dropout(params['dropout'])(com_drop)\n",
    "\n",
    "        prediction = tf.keras.layers.Dense(1,activation='sigmoid')(com_drop)\n",
    "\n",
    "        model = tf.keras.models.Model(inputs=[in_id,in_mask,in_segment,input_image], outputs=prediction)\n",
    "\n",
    "    model = tf.keras.utils.multi_gpu_model(model,gpus=no_of_gpu)\n",
    "    \n",
    "#     if params['optimizer'] == 'adam':\n",
    "#         opt = tf.keras.optimizers.Adam(lr=0.0005)\n",
    "#     else:\n",
    "#         opt = tf.keras.optimizers.RMSprop(lr=0.00005)\n",
    "        \n",
    "#     model.compile(loss='binary_crossentropy', optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])), metrics=['accuracy'])\n",
    "    model.compile(loss='binary_crossentropy', optimizer=params['optimizer'], metrics=['accuracy'])\n",
    "    initialize_vars(sess)\n",
    "    \n",
    "    out = model.fit(x_train, y_train,\n",
    "                    batch_size=params['batch_size'],\n",
    "                    epochs=params['epochs'],\n",
    "                    verbose=0,\n",
    "                    shuffle=True,\n",
    "                    validation_data=[x_val, y_val],callbacks=[live()])\n",
    "    \n",
    "    return out, model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_news_model( params):\n",
    "    \n",
    "    K.clear_session()\n",
    "    \n",
    "    with tf.device('/cpu:0'):\n",
    "        bert_base = BertLayer()\n",
    "        bert_base.trainable= params['bert_trainable']\n",
    "\n",
    "        in_id = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_ids\")\n",
    "        in_mask = tf.keras.layers.Input(shape=(max_seq_length,), name=\"input_masks\")\n",
    "        in_segment = tf.keras.layers.Input(shape=(max_seq_length,), name=\"segment_ids\")\n",
    "        bert_inputs = [in_id, in_mask, in_segment]\n",
    "        bert_output = bert_base(bert_inputs)\n",
    "\n",
    "        if params['text_no_hidden_layer']>0:\n",
    "            for i in range(params['text_no_hidden_layer']):\n",
    "                bert_output = tf.keras.layers.Dense(params['text_hidden_neurons'], activation='relu')(bert_output)\n",
    "                bert_output = tf.keras.layers.Dropout(params['dropout'])(bert_output)\n",
    "\n",
    "        text_repr = tf.keras.layers.Dense(params['repr_size'], activation='relu')(bert_output)\n",
    "\n",
    "        #image model\n",
    "        conv_base = tf.keras.applications.VGG19(weights='imagenet', include_top=False, input_shape=(3,224,224))\n",
    "        conv_base.trainable=False\n",
    "\n",
    "        input_image = tf.keras.layers.Input(shape=(3,224,224))\n",
    "        base_output = conv_base(input_image)\n",
    "        flat = tf.keras.layers.Flatten()(base_output)\n",
    "\n",
    "        if params['vis_no_hidden_layer']>0:\n",
    "            for i in range(params['vis_no_hidden_layer']):\n",
    "                flat = tf.keras.layers.Dense(params['vis_hidden_neurons'], activation='relu')(flat)\n",
    "                flat = tf.keras.layers.Dropout(params['dropout'])(flat)\n",
    "\n",
    "        visual_repr = tf.keras.layers.Dense(params['repr_size'],activation='relu')(flat)\n",
    "\n",
    "\n",
    "        #classifier\n",
    "        combine_repr = tf.keras.layers.concatenate([text_repr, visual_repr])\n",
    "        com_drop=tf.keras.layers.Dropout(params['dropout'])(combine_repr)\n",
    "\n",
    "        if params['final_no_hidden_layer']>0:\n",
    "            for i in range(params['final_no_hidden_layer']):\n",
    "                com_drop = tf.keras.layers.Dense(params['final_hidden_neurons'], activation='relu')(com_drop)\n",
    "                com_drop=tf.keras.layers.Dropout(params['dropout'])(com_drop)\n",
    "\n",
    "        prediction = tf.keras.layers.Dense(1,activation='sigmoid')(com_drop)\n",
    "\n",
    "        model = tf.keras.models.Model(inputs=[in_id,in_mask,in_segment,input_image], outputs=prediction)\n",
    "\n",
    "    model = tf.keras.utils.multi_gpu_model(model,gpus=no_of_gpu)\n",
    "    \n",
    "#     if params['optimizer'] == 'adam':\n",
    "#         opt = tf.keras.optimizers.Adam(lr=0.0005)\n",
    "#     else:\n",
    "#         opt = tf.keras.optimizers.RMSprop(lr=0.00005)\n",
    "        \n",
    "    model.compile(loss='binary_crossentropy', optimizer=params['optimizer'](), metrics=['accuracy'])\n",
    "    initialize_vars(sess)\n",
    "    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning rate finder on Twitter dataset best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_twitter_data = {\n",
    "    'bert_trainable' :False,\n",
    "    'text_no_hidden_layer':1,\n",
    "    'text_hidden_neurons':768,\n",
    "    'dropout':0.4,\n",
    "    'repr_size':32,\n",
    "    'vis_no_hidden_layer':1,\n",
    "    'vis_hidden_neurons':2742,\n",
    "    'final_no_hidden_layer':1,\n",
    "    'final_hidden_neurons':35,\n",
    "    'optimizer':tf.keras.optimizers.Adam\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=get_news_model(params_twitter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_finder = LRFinder(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4140/4140 [==============================] - 43s 10ms/step - loss: 0.6253 - acc: 0.7077\n",
      "Epoch 2/10\n",
      "4140/4140 [==============================] - 26s 6ms/step - loss: 0.5148 - acc: 0.7717\n",
      "Epoch 3/10\n",
      "4140/4140 [==============================] - 26s 6ms/step - loss: 0.4349 - acc: 0.8121\n",
      "Epoch 4/10\n",
      "4140/4140 [==============================] - 26s 6ms/step - loss: 0.3715 - acc: 0.8386\n",
      "Epoch 5/10\n",
      "4140/4140 [==============================] - 26s 6ms/step - loss: 0.4358 - acc: 0.8314\n",
      "Epoch 6/10\n",
      " 512/4140 [==>...........................] - ETA: 22s - loss: 1.1632 - acc: 0.7949"
     ]
    }
   ],
   "source": [
    "lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.0001, 10, 256, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEOCAYAAABmVAtTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJztvXmYHGd57n0/va8z0z2rpJE0o82yjXdJ4AUwYLNjdmOSj0AIIeTEhEBOEsghhHCSExJCFsKScAgnMQkxW/ACDl4AY2NsLMm7ZEnWrpE0+0xP7+v7/VH1Vld3V3dX93R198w8v+vSpZnq6qp3eqm7np2EEGAYhmEYALB1egEMwzBM98CiwDAMw2iwKDAMwzAaLAoMwzCMBosCwzAMo8GiwDAMw2iwKDAMwzAaLAoMwzCMBosCwzAMo8GiwDAMw2g4Or2ARhkYGBBjY2OdXgbDMMyKYv/+/bNCiMF6+604URgbG8O+ffs6vQyGYZgVBRGdMrMfu48YhmEYDRYFhmEYRoNFgWEYhtFgUWAYhmE0WBQYhmEYDRYFhmEYRmNNi8Lz55dQKPA4UoZhGMmaFYVTc3G87h8exoNHpju9FIZhmK5hzYrCdDQNADi7mOrwShiGYbqHNSsK0VQWALAQz3R4JQzDMN2DpaJARK8losNEdJSIPm7w+N8R0VPqvyNEtGjlevREUzkAwEKCRYFhGEZiWe8jIrID+BKAGwFMANhLRHcJIQ7KfYQQH9Xt/2EAV1i1nnKWVFFYTGTbdUqGYZiux0pLYQ+Ao0KI40KIDIDbAby5xv7vBvCfFq6nBM19xJYCwzCMhpWisAHAGd3vE+q2CohoM4BxAD+xcD0lxKT7iGMKDMMwGt0SaL4FwHeFEHmjB4nog0S0j4j2zczMtOSExZgCu48YhmEkVorCWQAbdb+PqtuMuAU1XEdCiK8KIXYJIXYNDtadEWEKdh8xDMNUYqUo7AWwnYjGicgF5cJ/V/lORLQTQAjAoxaupQJpKURTOWTzhXaemmEYpmuxTBSEEDkAtwK4F8DzAL4thDhARJ8hopt0u94C4HYhRFv7TUhRADgDiWEYRmLpOE4hxD0A7inb9qmy3z9t5RqqEU3rRSGDwaC7E8tgGIbpKrol0Nx2oqkshlQhmOcMJIZhGABrWhRy2BT2AeAMJIZhGMmaFAUhBGLpoigscgYSwzAMgDUqColMHvmCwKZ+thQYhmH0rElRkJlHg0E33A4b1yowDMOorElRiKUVyyDocSLsd3GrC4ZhGJU1KQqyQ2rQ7UCfz8XuI4ZhGJU1KQrSfRT0OBDyOdl9xDAMo7JGRaHoPgr5XCwKDMMwKmtUFHSWgt/JbS4YhmFU1qQoxErcRy4sJjIoFNraeolhGKYrWZOiEE1lQQT4XUqguSCApRRbCwzDMGtSFJZSOQRcDthshLDfCYAL2BiGYYA1KgrRVA5Bj9Igts/nAsDDdhiGYYA1KwpZBD2KhRCSosAFbAzDMGtVFIqWQlizFNh9xDAMsyZFIZbWuY/UmIJVnVL3n1rAb//7fuQ5u4lhmBXAmhSFaCqLgOo+CrodcNjIskE7jx6bxX8/N4m5WNqS4zMMw7SSNSoKRUuBiCztf5TI5AGwe4phmJXBmhcFAAj5nJa5j6Qo8CAfhmFWAmtOFFLZPDL5AnpU9xEAS/sfJdlSYBhmBbHmRCGWLra4kPT5nFiIW+Q+yiqiEEmypcAwTPez5kRBNsMLuIuiEPazpcAwDAOsSVEots2W9PlcWExkIUTr00aTWUWEuBMrwzArgTUoCpXuo5DPiUy+gLh6V99KONDMMMxKYg2KgrQUdKLgt67VRdF9xKLAMEz3s+ZEQc5nLs8+Aqxx8RQtBXYfMQzT/aw5UYhVcR8B1tzNsygwDLOSWHOiIGMKfreB+8gCUUhm1EAzp6QyDLMCWIOikIXXaYfTXvzTrWqfLYTQ6hQWLMpuYhiGaSVrUBRKW1wAQK/XCaLW1xKkcwUIobiqMrkCUtlCS4/PMAzTataeKKSzFaJgtxF6PM6Wu49kPGFDnxcAZyAxDNP9rD1RSOVKCtckSlVzay2FhBpPWNfrAcDBZoZhup81KgqOiu19FnRKlTUK61VLgQvYGKa7iKayyObZratnDYpCpfsIsKZTaqJMFLj/EcN0F6/5u4fw9Z+f6PQyKhBCINchsVqDopBD0F3pPgr5XC3vlJrMSlFQ3UeclsowXUMuX8C5SAoTC8lOL6WCh1+YxeWfud+SLgv1WJuiYGgptD7QLN1H63ql+4gtBYbpFmSvs4QFPc+Wy4nZOGLpHI5MRdt+7jUlCtl8Acls3jDQHPK7kMjkkcq27gMiP2whnwtep51jCgzTRcTV2SoyIaSbiKtrOj2faPu515QoyA9BoEpMAWjt3bz8sPlcdmWQD1sKDNM1FEWh+ywF6WVYdaJARK8losNEdJSIPl5ln5uJ6CARHSCib1q5HqO22RIr+h/JmILXZddmNjAM0x3EutlSSHdOFCqvji2CiOwAvgTgRgATAPYS0V1CiIO6fbYD+ASAa4UQC0Q0ZNV6AGBJbZvdY5iS2vr+R/IOxOeyo8/b+pRXhmGaR154u9FSkEJ1am51WQp7ABwVQhwXQmQA3A7gzWX7/CaALwkhFgBACDFt4Xp0loJRTEG1FFqYgSQ/bB6HHSF/6wPZDMM0j7QUkl0oCjIIfmaVuY82ADij+31C3aZnB4AdRPQIET1GRK+1cD013UdhCyyFZCYHj9MGm43Q63UhkmT3EcN0C/JuPN6F7iPZXXkuntHEq110OtDsALAdwPUA3g3g/xJRX/lORPRBItpHRPtmZmaaPpnRfGZJnxZobq37yOdSBCjkc1o2B5phmMbp5kCzdG0BwKm5eFvPbaUonAWwUff7qLpNzwSAu4QQWSHECQBHoIhECUKIrwohdgkhdg0ODja9IKm4AXelpeBy2OB32VuaIZTM5OF12gEo2U25gmi76jMMY0xMF1Potpu1RCaHgYByo9puF5KVorAXwHYiGiciF4BbANxVts8dUKwEENEAFHfScasWVMt9BCi1CvMtrCBULAVFFHrV7CbOQGKY7kBaCvmCQKbL+h8lMnnsHOkB0P5gs2WiIITIAbgVwL0AngfwbSHEASL6DBHdpO52L4A5IjoI4KcA/kAIMWfVmpZSWbjsNnjUu/dy+v0uzLVSFLJFUbByDjTDMI2jt9oT6e5yISUyeYz0etDnc7Y9LdWylFQAEELcA+Cesm2f0v0sAHxM/Wc51VpcSPoDbkxHUy07XzKTg1cVhT4L50AzDNM4cb0oZPMIdXAt5cQzOfhcdmwO+9ouCp0ONLeVeqIQ9rswF2tt8Zo+0AywKDBMt6DPOkp2WQZSIq1cOzayKFhLLJU1bHEh6Q8o7qNWBZ0SmbxmKfR6FfcRp6UyTHegz/CJd5H7KJsvIJMvwO+yY3O/D2cXkm1to72mRKFa22xJv9+FTK7QsgyhZCYPn7PMfdTi9twMwzRHPJ2D3UYAuistVeuE4HZgU9iHXEHgfKR1bu16rD1RqGUp+N0A0LIMJH32kdNuQ9Dt4JkKDNMlxNI59PsVC76b+h/pG2luCvsBtDcDaY2JQtawcE0SVvOCZ1sUV0hm8vC6iiLUqxawrSb2npzHQ0eaLyhkmE4Rz+Qw1KPcCHalpeCyY1O/D0B7G+OtMVGobSkMtNBSyKl+QWkpAEpa6mprivc39x7Grd98oiv7xzBMLeLpPAYDUhS6yFJQ4xt+lwMjPR647Dacmm9fVfOaEYVCQSCWqZN9pFoKc7H0ss+XyBbVXrIaZyrMRNNYSuVw19PlxeoM093E0jkMBZVRud1kKcR17iO7jTAa9ra1qnnNiEI8k4MQ1auZAWj+xVYUsMk7Z2+JKKw+S2EmqgjobY+e6rpWAQxTjWy+gEyugMFgN7qPVFFQ2/FsCvs4pmAFtdpmSzxOO/wue0vcR3q/oKTP68TiKkpJTWbyiKZz2BT24cC5JTx5ZrHTS2IYU0gXTcjvgt1G3eU+ykj3kXLt2Bz24fRcom03XWtQFGoXcfcH3K1xH6kfMq9TH1NwIpLMIl9YHXfUs+rr9OvXjiHgduAbj57q8IoYxhyxjGyOaYfPZe8uSyFdTEkFgI1hH6LpXNuSVNaQKFRvm60n3KL+R0X3UVGE+nwuCFFcy0pnWnUdjQ348fYrN+CHz5zXhIJhuhnZ4sLnciii0EXFa1pMQb2h3NyvpqW2Ka6whkTBnKUwEGhNqwtD95HW6mJ1iIIUgMGAG++5ejMy+QK+ve9MnWcxTOfRt9H3uRxaYkg3UCxeU64dm8LtTUtdO6KgfgiCBrMU9IRb1D5bvrGl7qPWD/LpJDLIPBh0Y9tQEFdv6cd/PHZ61bjHmNWLtBT8bmkpdFNMIQeHjeCyK5dnTRTaNGxn7YiCafeRG3Px9LKDOimDlNTVNlNhJpoGkSKkAPBrV2/G2cUkfnLI0lHbDLNsiqLQfTGFeFrpmUaktODwuuwYCrrZUmg1jbiPsnmhWRbNUnQfFc8XsmAOdCeZiaUR9rngVO9obrxoGCM9HnzjMQ44M92NnLqmuY+6KPsomcnD7yq9TrUzLXXNiMIbL12H//e+3SV37kbIu97lxhW07KOylFTAGkvhZ0dm8OPnp1p+3FrMRNNanjcAOOw23LxrFA8dmcHSKgmmM6sT+f3U3EfdZClkclo8QbKp39e2ArY1IwqjIR9esXNIM8mq0R+QrS6Wl0WTNAg093idILImpvC5ew/h8/cdaflxa1EuCgCwZTAAAJiNchYS071UBJq7SBT0jTQlm8I+nF9KIZ2zfp1rRhTMIqual9sUL5HNw2knzbUCAHYbodeCArZCQeDYdBwzbU4HnYmmtd4xkpD6+rVy1jXDtJp4OgcbAW6HTbUUusd9FE/nStzOALC53wchgImFpOXnZ1Eooz/QmotaMpMvyTyS9Hlb3//oXCSJZDaPuVi6bZk/QgjMxiothX4WBWYFEE/n4Xc7QERd5z5KZvNaNbOkmIFkvQuJRaGMYkxheXfdiUyl2gPW9D86Oh0DABQEMLdMt5dZoukc0rkCBthSYFYgsXQOATU93edyIJ0rdE0qdTyd06qZJXKuQjsykGqn4qxB3A47gm7HsquajfyCgFLA1so50EBRFADFpSM7P1qJvkZBT1jNsJpfJRlWzOokns7Br4mC8j1NZHJ1U9bbQUI3sVEyEHDh7luvw/ig3/Lzs6VgQLgFVc1J3XxmPSGfq+UpqeWi0A6qiYLXZYfXacd8i4WPYVpJTC8KbikK3eFCSmTy2tokRIRLRns168ZKWBQM6G9BVXM1S6HX60SkxTGFo9MxjPQo1kGnRQFQq8LZUmC6mEQmj4AqBkVLoVtEIVc3dd5KWBQMCPvdy27slsiWjuKUhHwuRNM5ZPOFZR1fIoTA0ZkYrt7aDwBty0DSRCFQRRQ4psB0MfF0TisQk7G/bshAyuQKyOYFi0K3MRBY/kUtmcnB66x8eUN+xWcZaVFa6lw8g8VEFi/aoJiW7bIUZmNpOO1Kim05Yb8LCywKTBcTM4wpdN5S0AbsGNxQtgsWBQPkne5y+h8p7qPKN7ZXq2puzUVTxhO2DQUwGHS31X3U73fDZqssBmxV+3GGsQol0Nx97iNtwI6bLYWuoj/gRq4gsJRs3pysFWgGWtfqolwUphsQhQ/82z7808+ONXXeGYMaBQlbCky3I+sUAJ37qAs6pbKl0KVoVc0mcv4PT0a1lhZ6ktnKtDKgOFOhVXfSR6dj8LnsWN/rwWDQbbq9xEI8gween8K+k/NNndeoxYUk7HchnslrnWIZppvI5ArI5AsIuLrPfRRPV7bHaTcsCgaETRZgfXf/BF7z9w/htkdPlmwXQiiiYPDGjg/44XHa8PALMy1Z67GZGLYOBkBEGAyYdx/tP7UAoHlxMmpxIZGv32rpBsusLvSzFIDuCjQbdVduNywKBshWF7Wqmu9++hz+8LtPA6isMkxlCxAChtlHQY8Tr3vROtz51LmW3Em/MBXD9iGlCd1g0I1oOmdouZSz95RiITTj5ikUBObimaqWgnSRtbpIj2FaQTxTbIYHdJelUOzeypZCV9HvVy521e6i7zswiY9+6yns2hzGlgE/ppZSJY8X/YLGb+w7rxpFNJXDvQcmTa0nmy/g4Lmliu3RVBaTSyls1YkCAFPptPtONm8pLCQyyBdEVVGQosqWAtONSBeNtBRkj7JuEIW4QXfldsOiYECtmQo/OzKDW7/5JC7e0It/ed8ubOr3YbJCFNRRnFXe2Jds6cdoyIvv7Juou5azi0nc8tXH8PovPIxHjs6WPHZsRhnPt61MFOoFm1PZPJ6ZWITLbkM0lUMm11jNhKyFqGcpcK0C043IttmyktlmI3id3dEpNblSAs1E9BEi6iGFfyGiJ4jo1VYvrlO4HDYEPY6Ki9rR6Sg+eNs+bBsK4LZf34Ogx4mRHg8mI6UX4aTBKE49NhvhHVeN4pFjs5hYqN7g6v6DU3j9PzyMw5NRBNwOfHvfmbL1FDOPAGBIvUjXiys8MxFBNi9wzTal4K3RO3p5/PJmeBLulMp0M/F0qfsIUNw1XWEpSCum20UBwPuFEEsAXg0gBOA9AD5r2aq6gIFAZVXzXU+fRzZfwL/++m5t3vJQjwdz8XRJhXLChAn4jqtGAQDf23+24rFMroDP3H0Qv3nbPmwMe/GDD1+Ht16xAT96brKk6O3odAxOO2Gz2lZ3UBOFVMUx9exVM45efdEIgMZ9/7VaXABKLYaN2isK09EUvvnL0207H7Ny0QLNuguvt0vaZxtNbGw3ZkVBVii9HsA3hBAHdNtWJUatGh46MoPLNvZhqKfYhXSkxwMhSu/OtTfWWV3tR0M+XLO1H9/ZfwYFXcveTK6AD9y2D19/5ATed80Yvvfb12BswI937hpFOlfAD585r+17dDqGsX4/HOogn36/GzaqbynsPTmP7UMBbFE7LjZrKVQTBZuNEPK1t9XF9584iz/+/rNtK95jVi4xA0vB5+yOOc3xjDKcy+XonGff7Jn3E9F9UEThXiIKAmhN854upd9f2il1MZHBMxOLeOn2wZL9RnqVC6M+rmA0itOIm3dtxMRCEo8dnwMA5AsCH/3WU3joyAw++7ZL8OmbLobboRzjkg292DEcwHf2F11Ix2ZimusIUCa7hf3umv2P8gWB/acWsGssrLl5Gg02z0TT8DrtFYNA9ITa3P9IikEkyS4rpjbFlNTi59fXJe6jZJVOCO3ErCj8BoCPA9gthEgAcAL4dctW1QX0B0pbNTxydA4FAbx8x0DJfsOq1TAVKYqCGfcRALzm4hEEPQ58Z/8EhBD44/96Fj989jw++YYLccueTSX7EilxiCdPL+LodAzpXB6n5uIlogCgbquLI1NRRFM57BkPFesxGmyiJ6uZa827Xk5TvHQuj+/sO9NQ00AphK3qKcWsXuKZ0uwjAF0zfU1p1Nc51xFgXhSuBnBYCLFIRP8fgE8CiFi3rM7T73djIZHRXDsPHZlB0OPAZaN9JfvJltVGlkI9v6DHacdNl63HPc+ex6fuPIBv7TuDD79yGz7w0i2G+7/lig2w2wjfe2ICJ2bjKAg0LAqygnnX5jD6fC5QE75/ozGc5YSX4T565Ogs/uC7z+D2vWfq76xbE8CiwNQnns7BYSO4dS4an8vRFaKQqNIep52YFYWvAEgQ0WUAfh/AMQC3WbaqLiDsdyFfEIgksxBC4KEXZnDdtgHNf6/fz2W3lYhCI/1L3rlrI9K5Ar7x2Cn82tWb8bEbd1TddyjowfU7BvFfT0zg8GQUALB1MFC2T21R2HtyASM9HoyGvLDbCH1eZ1Puo2rVzJJwoPlhQvNx5cL+Tw8eM20tFN1HLApMbeTUNb2lq1gKnY8pJDK5igE77casKOSE0jL0zQC+KIT4EoBgvScR0WuJ6DARHSWijxs8/j4imiGip9R/H2hs+dahVTXH0zg6HcP5SAov2zFYsR8RYajHXeo+qpOSquey0V68cucQfuXFm/DpN11c0yUDKFlLU0tp/OsvToKoUhQGg0pMwajDqxACe0/OY9dYSDtPLTfPQjyDP73zOURTpRfamWgaA0FXzXWGfS4sJLIlQXSzyAv72cUk7nzqnKnnaKLQ4gFGzOojls5XuGi6xVKIVxnO1U7MSlKUiD4BJRX1pURkgxJXqAoR2QF8CcCNACYA7CWiu4QQB8t2/ZYQ4tYG1205WlVzLIPn1Gril24fMNx3uMdTYimkMnkQocQ8rQYR4evv2216Xa+6cBghnxNPnl7ExrC3wtQcDLiRzQssJrII+Usv3GcXkzgfSWH3WFjb1u93VxWFh16Ywb89egrjA36879pxAEp21EIii8FA7TnQ0tJaSmXR56stIOUsqaKwcySILz94FG9V3WbVyOaVNQFAZBmdbZm1gX4+s8TnsndNl9R2zFivhVlL4V0A0lDqFSYBjAL4XJ3n7AFwVAhxXAiRAXA7FEtjRVC0FDJ46MgMtgz6MRryGe470uPB9JI+JTUPr9Ne966/GVwOG958+QYAwLYyKwHQ1SoYBI9la4tdYyFtWy1LYVK1fr61b0KzPObitdNR9cdV9m/chRRJZhF0O3DrK7fh+Ey8bjsQfZYYu4+YesQNXDQ+lx2JbH5ZM1RaQSLdeUvBlCioQvAfAHqJ6I0AUkKIejGFDQD0kcIJdVs5byeiZ4jou0S00cx62oFM1zy3mMQvT8zhZdsrXUcSaSnID1SiSofUViEL38qDzIC+gK1SFPaenEfA7cDOkR5tW7jGlLnzqig8f34JB1RrqV6NgnbcZVQ1L6Wy6PEqjQO3DPjxpZ8erfll1RcZsigw9YilcyU1CoDiPhJCaWbZSRKZfEermQHzbS5uBvA4gHcCuBnAL4noHS04/90AxoQQlwK4H8C/VTn/B4loHxHtm5lpTcvpekjXy70HJpHKFvByg3iCZKTXjUQmj6hqflYbsNMqLl7fg79464vwnpeMVTxWSxT2nVzAlZtDJa6Yfr+rJMtKz2QkhXW9HrgdNq3FxmydvkeSZYlCUhEFu43woeu34sC5JTx4uPr7Lv9WIhYFpj6JdL6iC2mxU2pnXUjxTE7rydQpzLqP/heUGoX3CiF+DYpr6E/qPOcsAP2d/6i6TUMIMSeEkFevrwG4yuhAQoivCiF2CSF2DQ5Wvzi3Eqfdhl6vE3tPLsBlt+HFW8JV9y2vVUhkcvDVqGZeLkSEX33xZmzqr3RnVet/FElkcXgqij061xGgNK8rCGDR4GI6uZTCtqEAXvuiEdzx5Fmksvn2WArJHHq9yuv31is2YEOfF1+sYS3INW3o82rxCIapRqxKTAHobKdUIYQ6xndliIJNCDGt+33OxHP3AthORONE5AJwC4C79DsQ0TrdrzcBeN7ketqCdCHtHg/VTC8tr1XoZK5xwO2Ax2mriCk8dkKpmtYHmYFi7GTeYMrcZCSFkR4P3rVrI5bUVt/yAtzvr5N9tAxRiCSz6PEoeQxOuw2/9fIt2H9qAY8dN54SJ//WrYMBLHJFM1OHeMbYfQR0VhQy+QLyBbFiKpp/RET3qimk7wPwQwD31HqCECIH4FYA90K52H9bCHGAiD5DRDepu/0uER0goqcB/C6A9zXzR1iFvGDWiicAwEivKgqqpZDsoNoTkWEB26PH5uBx2nDFplJLoXjxLr3DzuULmI4q7iPZ6vvb+85gJppGj8cBj8GoUT0epx0+l73pmEKvt5jcdvOujXA5bPjJoSnD/WeiaQTdDgz3uNl9xNQlns5VXHily6aT7qOE1iF1BaSkCiH+gIjeDuBaddNXhRDfN/G8e1AmHkKIT+l+/gSAT5hfbnuRF8zyfkflaO4jnaUgZzF3gsGAG9NlnVJ/cWwWu8fCFY22iqJQKiIzsTQKAhjp9cJmI7zzqo34uweOIJrK1XUdSUI+V1OT3SJqTEHicdox2ufF2cWk4f6zsTQGgm70ep2rRhSeOxvBxrCvRByZ5ZPO5ZHNCwTKYwpdMGgn3gWzFIAGhuwIIb4nhPiY+q+uIKwGtg0FMNbvw4XratfpeZx29HqdmFLTUpPZvOEoznZRbilMR1M4MhXDNVsr6yyqTZmTmUfrVCvoHbtGQaTMYjArCuX9o8yQzReQyOQrLoYbQl5MLBiLgqyw7vU6kcoWkM51vghpORQKAu/4p1/gaw8f7/RSVh3lU9ck3eA+0hppdnOgmYiiRLRk8C9KRJXzIVcZH71hB+75yEtN1RuM6ArYlEBz597YclF49JgST7hWHaqjJ+RXLr7zZTMVpCtMWkEb+ry4btuAenxzxTUhX+OtLmSguFwURkO+qqKgWAou7Tkr3VqIpnJIZQs4NVd9ABPTHMUOqd3nPtIa9XWzpSCECAohegz+BYUQPbWeuxpw2G2mTbnhXk+J+6iTTa0GAx4sJLLamM1Hj80h6HHg4vW9Ffu6HXYE3I66lgKg+PaV45u0FMraj5thKaV8KXu8pa/7aMiL+XjG8EsrLQXpclrpGUhS1M5HjEWQaR7poqkMNHfefSQrqldKQzymDiM97q4INAPAUI90CSnWwiPHZvGSLf1VW0UYVTVPLaXgdthKYiOvvngYl432YndZWms1Qv7GLYVIVUvBCwA4W2YtpLJ5LKVyGFDdR/pjNMrEQgKfuvM57W6yU8gMqnOLtSfoMY1T1VJQb/7qvffZfAF/9aNDODPfeituRVgKjHlGejyYjaWRzOSRK4iOioK8k5+JpnFmPoEz80lcu7XSdSQJG1y8z6uFa3rXmdthx523XofXXbKu/BBVj5vI5JHKmr/7knf5MiVVIkWh3IUkLZzB4PJEYSGewa99/XHc9ugp7D+10PDzW4lc/+RSCvkmGgoy1YmpMYWKQLP6fU3WsRQefmEGX3nwGO47aJwJtxy07srdHFNgzDPc60FBAKfVO4hOB5oBYHoprcUTrtlm3MwPMHbzTEaSWqptszRTq1DdUlAK9SbKMpD0xXT1ROGvfnQIn7n7YIULKpXN4wO37cPJ2TiA0tkYnUCuP18QPF60xUhLoNwt7LTb4LLbtA7H1bj7aWUc7mKTbeFrkWBLYXUhC9hOqBeWjloCYQw8AAAgAElEQVQKuqZ4jxybxUDAhe0GfZIkRu4jxVLwLmsdzYjCktqmu6dMFAYDbrjsNkwslJrts+pFs8R9VKV99rf3nsHXHzmBN3zh53jqzCIA5cL7kdufxBOnF/D5my8DUDpFrxPoRa1aGi7THEbzmSXeOp1Sk5k87lObM1oxajbOMYXVxXCZKHg7mH0ki+6ml9L4xbE5XL11oPboTLUpnmwjUSgITC2luspSsNkI6/s8Fe6jGV0vph7NUqj8YmfzBczFM7jhwiGks3m8/Su/wD888AL+7O4DuPfAFP7kDRfhrVeMos/n7BpLAeBgc6upFlMAlKKxWoHmnx6eRjyTh91GloiC2dnuVtNZO2UVIUVBuiA6qfZuhx19PiceOz6HmWi6ZjwBUAbiZPIFxNI5BD3KJLZsXmjWT7M0Kwouu81wFoVRWqq0FPoDLjjtNvhddkP3kXSPvXLnMD5/8+X40zufw989cAQA8JsvHcf7r1PmRYz0FLPIOkUkkQURIARwnoPNLaUoCpXfT28dUbjrqXMYDLqxKeyzxlLI5OFy2OC0d/ZenUWhRfT7XXDaqSvcR4DSGE/2OzIqWtOjv3gHPU4ti2rZloKvCfdRMocer9PQshkNefHA89Ml22ZiafR6nXA7lNe7WlWzrPCWsYe/v+UKvPriERybjuF3XrFN2698YFIniCSzGAi4EU/ncI4thZYSz+ThtJP2edGjTF8zdh9FU1n85PA0fmXPJpyPJLXveStJZHIdv24A7D5qGTYbYSjowfEuEYXBoBtCKEVnG8O1YwPFpnjKxVteFNctUxR6vU7YqFFRyFbUKEg29HkxG0uXZDPNRNMYCBSb8/VUEQUZsB3SVWO//pJ1+PCrtsOmS9Ud6fFgMrK84G40lcVCPINUk0NbIsks+rxOrO/z4hzHFFqK0dQ1ic9l19JCy7nvwBQyuQLedNl6NQbX+lqYeLrzsxQAthRaynCPG0+cVgKYXgtbZ5tBpqVes7W/bkV2WG11oYmCene6XEvBZiOEfC7MN5CpUd4MT89ouJiWKgcMzcbSJW03er1Ow+K1aZMtv4d7PZiLp5HNF5oy42djaVzz2Z9ohYNESnzpd1+1HR96+VZTx4gkldfA67JrRYRMa4ilc1UvvD6XHbNVii3vfuYcNvR5ceWmPvzk0BQWEkoMzky3g8VEBh//3rP487e+CAM1Cj+TWbYUVh36i2in31x58bu2RiqqpL9sdOb5SAoOG2HAb65yuRZhv6uihUYt9G2zy9HSUnUZSIqlUCoKtSyFWl9KQLGOhCiKSKOcnI0jkyvgPS/ZjD987QX48Cu2Iehx4LHjc6aPsZhQRGF9r5cL2FqMYikYfzd9bmP30Xw8g5+/MIs3XbYeRMqNjjJ/3FyR41NnFvGjA5N45OhsnbV1fpYCwJZCSxnu6R5R2Bj2wWEjXF0nyAxUBoQnIykM93hK3CrNEvI3aCkksxjr9xs+plU161wqs7FMhaVQLaYQ8jkrusSWo83GiKSwoa/xlFx5Z/+rL9mkjT19eiLScLB950gQ61V3WTqXN/SBM40TT+eru4+cxoHm/37uPHIFgTddphRt6r8vZrrYys/jsZnacQglptD5SzJbCi1En63T6Vzjm3dtxH9/5KUlQlUNn8sOl8OmXbhkNXMr6DeogahFpEZMYSjogdNOWgZSMpNHLJ0zJQoz0TSGTDTyK2+D3ijyeet6ioJiVAdSi6VkFr0+J9b1lc7pYJaP0Xxmid/tMBSFu546h62Dfly0ThH5UINZddKdeXwmVnO/RKZyTGgnYFFoIaWWQmcV3+O0Y/tw7ZbfEiIqqWqeXEphuEWiEPKbn6kghGKSV7v7stsI63qLLbTlvOhy91Eym9d8+pLpaNpUy2/pAmzWlz8ZScHjtJUIWyOikMsXEE3nNPcRwD2QWkm8RkxBSUktdQlNRlJ4/OS85joCill1Zj/XEU0U6lkK+Y5fNwAWhZYiRcHlsFVtPtetyP5HQgicjySxbpk1CpJ+9bgFEz184pk88gVR0yQfDXlxVo0pGAWPe33GrS4US6G+KEgXU7OWwuSSMsJUH4BspAeU9FP3ep1Y3ycFijOQWoVyN17dfZTNi5Ibivufn4IQwBsvXa9t09xHJt2imijMxmp+D5SJcGwprCrkXWY3vLGNEvYrA3GWkkov/+VmHklCPhcKwlyTumrN8PSM6obtaH2PyiwFoPR8QgjTlgIRYVjX8bZRppZSFS47eREx0zFWX9Et24xwBlLrUNxH1QPNQGlTvMdPzGOkx4Otg8U4l/Z+NmgppLIFnK9xs5FkS2H1IWMKnRyw0yyK7z+N80vKBXe5fY+04wZKM5tqUa3FhZ7RkA/TUaVWYTZWaSn0GIjCUiqHTK5gemLcyDIK2CYN2oOEVHeDmdkSct19PiUlNeRzcv+jFiGEqFunAACJbE7bf++JeeweD5dYfloMrkFLAageVxBCIJ6pnhnVTlgUWojXZUePx9HxIHMzhP1uzMcy2l1pKy0FwFxQTrMUaoiCzAg6t5jULAV55wYUBUVfqzCjq2Y2w3CTrS6EEJiKpCteOymMjVoKgCLO51kUWkI6V0CuIOqLgmopTCwkMbmUqpgfQkQI+8ynWkeSWYz1K+nU1eIK6VwBBdH5BBWARaHljPR6usIEbJSw34l4Jo9TakV2q7KPiul79fP+zVkKxbTU2VgaYb+rpMjMyH00rVUzm/ub1vV6MBlJNVyNPB/PIJMvVPSMakQYy1+D9X1edh+1CK3vUZULrzanWZ25sPfkPABg91i4Yt9GBkhFkjlsGwog4HZUtRSKa+v8tYNFocVcNtqnVduuJGRV84FzS7CR+bvqemhtvE0Ug2mjOGvFFMKygC1Z0eICMBaFGZPVzJLhHg/SuULDw3qky6lcFPobSGGMqBeaHk0UPOw+ahFx9WJfzVLwa5aC8jnce3IePR4HLjDI4gv7nQ2lpPZ4ndgy6K9aq5Dokg6pABevtZzPvfOyTi+hKeQd/cHzSxgIuFvWqXEg4IbdRqZ89GYsheGgGw4bYWIhgZlYZfC4lijIMaX1kO6fyaUU+nyuOnsXkS6n8nTeRnpAGbmPoqlczfx6xhy1ZikARdeNvEA/fmIeu8bChkWcIZ8L5xaXTJ1Xti3ZOhjAL6tUtmsDdrrgPWZLgQFQ9HsfmYq2zHUEKLUFQ0G3qSZzMg4Q8FT/YjjsNoz0KnMVZmPpkswjQJmg5Strnz0dTcPtsCFo8gunr2puBPk3llsKWg8ok6Lgddq1CmYtLZWthWUTz1SfpQDo3EeZPOZiaRybiWNXlXnkZmtPcmpL+l6vE1sG/DgXSRm20pBr45gC0zVISyGbFy0LMkuUdtT1L2qRZBZBj6NujYdMSy3veyQpr2qeiaYx1OM21bxMrhdovKp5MpIEVXG9hUxeRORdpWS9DKyv8rjCJ+94Fg+/MGPpOer1v5Kum3gmh33qnO49BvEEQPm+RJJZ5PIFw8cl+rqTLYOKW9mo7XayS0ZxAiwKjEq/LoOnVemoxeN5TN11L6WqN8PTMxry4chUFKmscZppuShMR1MVFkUthjVLobGmeJNLynmMXG9m7yxlMzyJtNpWcwvt+XgG//7Yadzx5DlLzyMbKW4IGX++pSgkM3nsPTEPl8OGS0Z7DfeVN1GLdeJOenfgFrXWwSiuUJwdzZYC0yX0eJzaHbolloIZUUhWb5utZzSk+NkB47s+Q0vBZOYRoFSk9/tdpqwbPZNLlemoknAD7iP9azDc44GNVrf76PBkFIBS8WslEwtJ9HgcVT9j0q2UyOSx9+Q8Lt/YV7URodmMsiWdKIwP+EFkXKvQTYFmFgUGgPR7y+Bma0VhXa8H8Uwe0VT9u6pqzfD06LuXVrMUlspiCo1mU5kVMj1TkcpqZkk4YC6FMaI2w5M47TYMBT2r2n10ZEoVhZl4U0OJzDKxkNTarxvhdthApPTUeu7cUkV9gh6zo2b1loLHaceGPq9hrUK9eEc7YVFgNOQHfbmzmcuRd8/1fPRLyerN8PTov9j1LIV0Lo/FRNZU3yM963o9mFxq3H1U7bUL+1xYSGTr9oAyspbW9XlWdf+jw6ooRJJZS2YfSyYWElqdixFEBL/LgUeOziJfEIb1CZKQyaZ45dlkWwYDhhZRki0FphuRotDqmIK8UNYrwqo1YEeP/otdL6YgJ2k1bCn0NlbVnMzkEUlmq7uP/HIwS21radFAFJSxnKvYUpiMwqG6Lq2YfQwo1eb1LAVAyf45NBmFjYCrNpuwFOpYfxWiMOA3tIhkDUU3FL6yKDAa/WoBm9l8frNoef91RKHWKE4963o9sNsINiptcSHp9TqRyOSRzRcarlHQ1tzjwXw8g3SufmdToHrhmkSus1YPqGy+gEQmXykKvR6cW0xa6loBlAvn3z9wBM9ORCw9T/k5D09FcY06IbBee+lmWUxkkcjkqwaZJbKA7cJ1PQjWuEEJ+ZXHzFoKshhx66AfiUy+om4nkcnB3SXdlVkUGI2xAR829/vgaXFDv2ETef/VLohGOOw2jPR4EPa7Db9E+vbZ0+qXbzDQmEtMXtynTbqQJuv0jDLTWVPfDE/Pul4v0rkCFhKtHxav5yeHpvH3D7yA7z951tLz6JlcSiGayuFVO4fgsttwrEqwef+pebz1y48Y5vibQXbWreU+AgCveqdey3UEAG6HHQG3A/Px2u/JUjILt8OmfadkWmq5+NVq6d1uWBQYjQ+/cjvu/J1rW35cj1Pp9lmrqtlMMzw9G8PeqnECfVXzTKw5S2FYV9VsBq2aeRmWQrWKblnAZmVaaqEg8Df3HQFgrk9Vq5CZRxeu68Hmfl9VS+FHz03iydOLODLVXIaSTEetJwrSp19PFADFWqj3WpVnk23VRKH074hnumOWAsBtLhgdHqe95VaCpF42j5kWF3o++YaLkM4ZFw7p22dPL6VBVFqHYYZGq5o199EyLIXFhLEwrtd1hn3RBuO8+eVyz3Pn8fz5JThsZKrNeauQmUc7hgMYH/DjWJWGcc+eVVxaJ2fjuHxjX8PnKVoKtWMKmiiMV48nSMI+F+brWG8Rte+RZLjHDb/LXlKrkMzkse/kQsPJEFbBosC0BSWbp4alIJvhmUhJBVDz4lhuKfT7XXA02MtppMGq5slICgG3o2pfHTOWwlIVYbR62E4uX8Df3n8EO4YDWN/nNdW8sFUcnoxhuMeNPp8LWwYD+OnhaeTyhZL3q1AQeO6s0meo2UD0xEICwRo1CpLBgBvbhwKm6lpCuhG21Si3FIgI44Ol4vd3DxzB6fkEPvv2S+qesx2w+4hpCyN1snkatRRqoZ+pML1k3AqjHj1eBzxOm3lLIZLCcA0Xlcdph89lNxdTKHsN+v0uuOw2nLMoLfX7T57F8Zk4PnbjBRgIuC1NCy3nyFQUO9QupFsG/cjmBc4slP6dJ+fiWjO75kWhfuYRAPzJGy/CN37jxaaOaaYgsVwUAGDLQEBzkz19ZhFfe/g43r1nE67ZOmDqvFbDosC0hZEeL2Zj1bN5zIziNEuJpRBNYaiJugsiMpzAdudTZ3H746cr9p9cStVN5a3XFK+aMNpshHV9HkvSUjO5Av7hxy/g0tFevObiYfSrY1mtznQCgHxB4IXpqNaaWo68LPe3S9fRUNCNk3PLEYX6qdYhv8t0Rb+ZmQqGojDox7lIEkupLP7wu89gKOjBJ16/09Q52wGLAtMWRnqVu+hq2TxWWAqRRBYz0cpOqmYZKevZdG4xiT/63jP4i3uer2iEZjSbuZz+gKtmXnt5+qKedb0eS1pdfGvvaUwsJPH7r75AmSjmdyGTKyCeMZeKuxzOzCeQyhawY0S1FAaMM3OenYjA7bDhhouGcWK28apnIQTOLpoThUYI+11IZPJIZau/VkaisHUwACGAP/zOMzg8FcX/eduLWnIz1CpYFJi2MKLeRVeLK8iiLrPZR7WQ7bMXEkpModm6i3JL4a9+dAipbAHRVA5PnF7UtucLAtPRtCZ81ahnKSwmsvC77IYN9Tb0+Vo+bCeZyeMLPzmKPWNhvGy74rrQirJMjppcDofUzCNpKYT8LoR8zoqK32fORnDhuh5sGwwgmso1HAiPJLOIpXMl7VFagZY8UEXo8wWBaCpX8ZmWjfF+dGASb7l8PV65c7il61oulooCEb2WiA4T0VEi+niN/d5ORIKIdlm5HqZz1MvmiSSzcOnyuZdLr9eJ0/NxZPOiaUthuNeD6aU0hBDYf2oBdz51Du+7Zgx2G+HBw9PafnOxNPIFUbc9SH+dTqlGd5WS0ZAXk0spZKpkXDXD//vFCcxE0/ifr7lAaysu52rMtSEtVWYebR8uTircMhgoycwpFAQOnI3g0tFejA8oF9OTDcYVzGYeNYpsdVEt2Cx7fZW/p/Lv6Pe78Kk3XdzSNbUCy0SBiOwAvgTgdQAuAvBuIrrIYL8ggI8A+KVVa2E6T72q5qVkrqUmdK/XiaPTyh3nciyFTL6AuXgGn/nBQQwF3fiD11yAqzaH8NPDxd7/k3VqFCT1ZioozfCMU2c3hn0QonW1CgvxDL7y4DG8aucQ9owXc/LlWNZ2BJsPT0WxKewrae0g20BIjs/GEc/k8aINRVFoNNhstkahUepZCtVcoj6XAx951Xb847uvMKzI7zRWWgp7ABwVQhwXQmQA3A7gzQb7/W8AfwVg9TZ3YdDjccDrtFd3HyWz6DWZjmrqfF4nTs8rF4OmYwrqRf6ff3YMT59ZxB+9dif8bgdeccEQnj+/pGVTyVTReoHmej7oWq+BvKBNLLRGFL7406OIp3P4o9eVBjj7TaTOtoojk8XMI8mWwQBmY2nNnficGmS+dLQXoyEvHDZqQhSU12xjiy2FsNrqopqA1oqTffTGHVprj27DSlHYAOCM7vcJdZsGEV0JYKMQ4ocWroPpAohICdzWiCm0Ip4g6fU6IRuSNpN9BBSrmr/28xO4dLQXb71C+fhef8EgAOBnqrVQnM1cW3zqtVteTGZquo+A4l3vcjgzn8Btj57EO6/aWHFRNtsSermkc3mcmI3jgpFAyXbpbz+hWgvPTETgcdqwbTAAh92GjWFfwxlIEwtJBN0O0zUwZqnXKbWVyRPtpGOBZiKyAfhbAL9vYt8PEtE+Ito3M2PtyD7GOkZqVDXX8qc3g/5YjXZIlUhLQQjgU2+8SBvgvnMkiJEeD36qxhUmIyk4bIQB//JEodZrMNKjNAFshaXwN/cdht1G+OiNOyoe87nscDtslovCidk4cgVRIUpaWqoabH7ubAQXrevRitnGB/w4MduYME4sJLAh5DU9jtUsfT4XiFC1qplFoZKzADbqfh9Vt0mCAF4E4EEiOgngJQDuMgo2CyG+KoTYJYTYNTg4aOGSGSspT/HUY7ZttlnkF9HnsletMq7HUNANj9OGN166Drt0vXCICNdfMIifvzCLbL6AyaUUhoJuTTSqYUYU+qrEFBx2G9b1epZtKTx3NoI7nzqH9187bpiPT0RKrYLF2Uey59EFI6WisCnsh91GOD4TR74g8Ny5CC7RVa+P9ftxssG0VLOFa41itxH6vE62FBpgL4DtRDRORC4AtwC4Sz4ohIgIIQaEEGNCiDEAjwG4SQixz8I1MR1EVjUbDZoxO4rTLPJYzVoJgHIhvuvW6/C5d1xW8dj1Fwwhms5h/6kFpUbBRMFTrcBkKptHKluo+RpsDPkqqn0b5bP/fQghnxMfun5r9XUGXJY3xTsypcxQkLUJEpfDho0hZTrZidkYEpk8Lhkt9joaH/Ahmc1jymT3WiEEzposXGuGWskDLAplCCFyAG4FcC+A5wF8WwhxgIg+Q0Q3WXVepnsZ6fEgVxAVQUwhBJZS5qaumUUea7lNxnYMB+E16F557bZ+OGyEBw/PYDJSfeKannCNFEYzXWJHQ95lWQoPHZnBz4/O4sOv3F7TKgv7rW91cXgyhvEBP1yOykuQkpYawzPqXIcSS6HBDKSlZA7RdM4yUajV6iKSzMJlt8HjXFnlYJauVghxjxBihxBiqxDiL9RtnxJC3GWw7/VsJaxuqqWlxjN55AuipYHAVlgKtQh6nNg9FsaDh6cVUTBhKfR6nbCRsaVg5q5yNOTD1FLa9OCfcv7riQkMBFz41ZdsqrmfbHVhJUemololczlbBvw4ORfHMxMReJ12Lc4AFHP8zQabz1iUjiqp1epiSe2Q2upYhtWsLAljVjRaAVtZBpIVZnbRUmjtvGk9118wiEOTUcQzeVOWgs1GCPmML7jVmuHpkRe2Znsg7T25gD3jYbgdtQsEw3XqKZZLIpPD6fmEVslczvigH6lsAfcfnMJF63tKOqau7/XC5bCZthSsKlyT1LMUWplm3S5YFJi2MVJlcE0rm+FJeiy2FADgFTuHtJ/NNlEL+12GgUlzloIiCmfmG3chnY8kcXYxiV2b6w+PMdPTZznI9hY7hgOGj8s4w9nFZInrCFCEdXPY14AoWGsphAOKpWAU+G51Rl27YFFg2sZAQBmfOVnWAtoKS2FAbdewzuTFuhm2DwWwXj1+vWpmSaiKa8bMa7AxrNztNpOWuu/kAgBg11j94TFWF7Dde2ASDhthz3i/4eN6d1G5KACKC8lsq4uzi0kE3PXnKDRL2OdCNi+01t56WBQYpg52G2Eo6MZkpDRzpNFRnGbY3O/H135tF95w6bqWHbMcIsL1qrVgxn0EKBdcI0tBTl2rdREZ7vHAYaOmgs37Ty3A67TjwnU9dfe1sileoSBw91Pn8LIdg1VbPAwG3Voa8aWjxqJwai6BvEEWWzmyZbZVfv2QNlGvslZhKdna5Il2waLAtBWlqtl6SwEAbrhouK7/fLm89+oxvHvPRu0uvh7VUhhrtc2W2G2E9X3e5iyFU/O4YlOfYQfWcqxsivf4yXmci6Tw5svXV92HiLBl0A+fy64NutczNuBHJl8w1QfK7ByFZpGtLoxeq/JRnCsFFgWmrRhVNWujOLuop7xZLhgJ4i/fdinsdQrXJP1qtkp5rUYkmUXQ46h7nNGQV8uoMUssncPBc0vYtbm+6wiwtineHU+ehc9lx40X1W4X/aZL1+PmXRsNX4+xfvMZSBMLCcuCzICu1UVZBlKhILCUYvcRw9RFKWArvauKJLMgAoKelZep0SghnwsFUbQMJGaL9zaGfA1bCk+dXkRBoKQquxZW9T9K5/K459nzeM3FIyWdUY34zZdtwadvMm4rrfVHqhNXiCSziKZaP0dBT/G1Kn0/o+kchFh5hWsAiwLTZkZ6PIilc1qv+UJB4MnTC+jzOuu2iVgNSNdM+QS2RZOiMBryYiaabigzaO/JedgIuGJTX/2doXS0ddqp5YHmnx6awVIqV9N1ZIahoBs+l72uKFideQToYwqlr5UVcbJ2waLAtBWZuik7i375waN4+IVZ3PrK7Z1cVtuQ7obyu3CzmSqjYeUC18gUtv2nFnDBSA+CJt1zREo9RasDzXc+dRYDAReuW2bLaCLC5v76GUhW1ygAQNCtCGi5yK/UFhcAiwLTZmSWzvlICj85NIXP338Eb7l8Pd5/7VhnF9YmqrlmlGZ4ZiwF5QJntlYhly/gydML2G0iFbV8na20FJZSWfz40DTeeOn6kmK0ZlGqnmu/Bmc1UbDOUpACWm4psCgwjEmkpfDY8Tl85PancOFID/7ybZeuuFYAzVJLFMy6jwDztQqy4voqk0FmSX+Lm+L96NlJZHKFZbuOJGMDPpyeTyCbrz6e9MxCAn6X3ZTYLoeBgFsbtCRhUWAYk8giry/99BgcNsI/v+cqw4ZzqxUjURBCIJIwl744HPTAaTc/V2HfyXkAwG6TQebiOlvbFO+Op85ic78Pl280F9eox1i/H/mCqPk6nJ5LYGPYZ/kNx6WjvXjy9EJJRhmLAsOYxOO0I+RTGsN98VeuNJ3fv1rwOO3wuewlF9xUtoBMvnbbbInNRtjQZ75b6r5TC1jf68H6BjNwwj5n0+6jXx6fw38+fhqPHpvDZCSF85EkHj0+hzdfvqFlF2iZgVQrrnB6PoHN/dZ/vnaPhbGUyuHIdFTbtpJFYfXnADJdx29cN47hHg+u7dIZtVZT3v+o2AzP3BD3UZNpqUII7Du5gN3jjVkJyhrdiKZyyOQKhu2tq5HLF/Chf9+PBd00MoeNIATwlha5joBircKxmVhJDypJoSBwej6hjU61EmmF7T25gJ0jSsV4JJmFw0bwrUArmEWBaTtrJdOoGuVB3EbvKkdDXjzw/FTd/c4uJjG5lGo4yAwojd4ApSjLbF8nAHji9CIWEll85s0Xaz2KTswm0B9wGVYnN0vY70KPx1G1gG0mlkY6V8Cmfr/h461kY9iL4R439p2cx3teshlAMUa0EmNlLAoM02b0ramPz8TwnX1nADQmCrOxDJKZfM14jGyC12iQGdA1xYs1JgoPPD8Fl92Gt105ioDbgZdut+ZOnYgwPhioWqtwSs1M2twG9yQRYddYGHtPzGvbVmozPIBFgWHaTtjnwt4T87jhb3+Go9PKgPpLR3tx4Trj+QLlyDjM2cUEtg1Vf86+U/MIuB2aS6OhNTZR1SyEwP0Hp/CSrf1Nz8VuhC0Dfvzy+JzhY6fVlN1NbYpZ7d4cwg+fOY+zi0ls6PNqA3ZWIhxoZpg2s304iFSugKGgG59+00X4+R+9Anfdeh36A+ZmP2hzFerEFfafWsQVm/pM92XSU2yfbT4t9dhMHCdm47jxwkofvxWMD/hxLpJCMlNZ3X16Lg4bARssrFHQI+M20lpgS4FhGNN86OVb8P7rxpru4CoL2CZqFLBlcgUcnY7i+gu2NHWOZiwFGed41YW1m921Cv1ozvKW4KfmE1jf5zXVFbYV7BzpQdDtwN6T83jLFRsQSWa1YPhKgy0FhmkzRLSslt6DATdcDlvNDKTjszFk8wI7q8xBrkefzwWiBkXh4BQuXt/TcPprs0hRMPPRXy0AAA5jSURBVIortCsdVWK3Ea7cHMLekyvfUmBRYJgVhs1GGK0zV+HQeSVn3sxQHSPsNeZJGzEbS2P/6YW6LbFbSU1RmEu0LZ4g2T0WwpGpGBbiGdNdb7sRFgWGWYFsCNUuYDs0GYXTTtqFsxnCfvNN8X5yaBpCADe0yXUEAH63A8M9bhyfKRWFWDqHuXgGm8Ltdd/IeoWfHZlBYYW2zQZYFBhmRTIa8tUMNB+aXMK2oeCyfOrhKlPijHjg4BTW9Xpw8frmLJNmGR/w48RsrGTb6bn2Zh5JLtvYB6edcL8aW2FRYBimbYyGvJiPZwwHxgOK++jCJuMJkn6/y1T2USqbx8MvzOKGC4fbXqw1PlBZqyDTUdsZUwCUFiaXbOjFQ4dnAKzMWQoAiwLDrEhkTcOzE5GKxxYTGUwupXDBMkXBrKXwi2OzSGbzbY0nSLYO+rGQyJa0DTk9r4hEJ/pq7R4PI6oKdY93ZSZ3sigwzArkqs1hEAGP66poJYcmlSDzziaDzJJ+vwuLySzyZfOky7n/4BQCbgdevKXxHkvLRQs269pdnJ5PoM/n7Ij7Zvfm4muwUt1HK1PKGGaN0+t1YudIDx4/OQegtJfUofNLALBs91HY74IQSv+jAV1h3Zn5BGZiaSwmMlhMZHH/wSm8fMfgstJsm0UThZk4rtyktPM41YHMI8kuXZ8pFgWGYdrKi8fD+NbeM8jmCyUB5cNTUYR8TgwGzVVIVyOsCsF8vCgK33j0JP7kzgMV+77pstZ1QG2EjWEf7DYqiSucnk/gkg29HVlPn8+FHcMBHJmKsSgwDNNe9oyH8a+/OInnzkZwxabiHerz56PYOdKz7KCvvikehoFIIou/ue8I9oyH8dsv34o+nxMhnwvhgAs9Juc/txqn3YZNYZ8mCrl8AWcXknjjpes6sh4AePF4P07NJdrS/8kKVuaqGYbR8uIfPzGviUKhIHB4Mopb9mxc9vHLW1188acvYCmVxZ/ddHHTRXFWMD7gx3FVFM5HUsgVRMfcRwDwezdsx5suW78i22YDHGhmmBXLYNCNLQN+rbUCoLhOktl80+0t9PRropDGmfkE/u0Xp/COK0e7ShAAaHMb5GAdAG0vXNPTH3BjTxODjboFFgWGWcHsGQ9j78nifGAt86iJdtnlhLROqRn89b2HYbMBv//qC5Z93FYzPuBHMpvHVDSlzVHY1OYahdUEiwLDrGB2j4URSWa1+cCHJpdABOwYXr6l4LTb0ONx4GdHZnD30+fwgeu2YKTX/MCddrFFl4F0ej4Bl92GkQYGAzGlsCgwzApGuilkvcKh81GM9ftrTmRrhP6AG0+eXkS/34XfenlzbbitZnxQEYXjs3Gcno9jNORtaoYEo8CiwDArmNGQF+t7PfilKgqHp6ItiSdIZLD5927cgWCHMozqMRz0wOu048SsYimw62h5sCgwzAqGiLB7XJkPnMjkcHIu3pJ4gmTLgB87R4K4Zffys5mswmYjjA34cXwm1tHCtdUCp6QyzApnz3gYdz51DvcfnIIQWHbPIz3/522XIF8QbZtg1ixbBvx49Pgcoqkci8Iy6e53mmGYurxYjSvc9ugpAMVmea3AabfB42x/+4pGGR/wa/UULArLg0WBYVY4WwcDCPtd2H9qAT6XHRtDa++iqB8mtHmFzkbuFiwVBSJ6LREdJqKjRPRxg8c/RETPEtFTRPRzIrrIyvUwzGqEiLBbbcS2YzgI2xrMvJEZSACwMdyeGdGrFctEgYjsAL4E4HUALgLwboOL/jeFEJcIIS4H8NcA/taq9TDMambPeD+A1rqOVhKyVmEw6IbPxaHS5WClpbAHwFEhxHEhRAbA7QDerN9BCLGk+9UPoHbjdoZhDJFxhW5rQdEu+nwuhHxObOZ4wrKxUlI3ADij+30CwIvLdyKi3wHwMQAuAK+0cD0Ms2q5eH0PvvyrV+L6CwY7vZSO8a7dm7C+jyuZl0vH7SwhxJcAfImIfgXAJwG8t3wfIvoggA8CwKZNm9q7QIZZARARXn9J59pFdwMff93OTi9hVWCl++gsAH3Fy6i6rRq3A3iL0QNCiK8KIXYJIXYNDq7dOyGGYRirsVIU9gLYTkTjROQCcAuAu/Q7EJF+juAbALxg4XoYhmGYOljmPhJC5IjoVgD3ArAD+LoQ4gARfQbAPiHEXQBuJaIbAGQBLMDAdcQwDMO0D0tjCkKIewDcU7btU7qfP2Ll+RmGYZjG4IpmhmEYRoNFgWEYhtFgUWAYhmE0WBQYhmEYDRJiZXWWIKIZAKeaeGovgEiLl2PFOZo5RiPPMbtvvf1qPV7tsQEAsybO3Wms/qy06vjd8Fmx4nMCrIzPykq7pmwWQtQv9BJCrIl/AL66Es7RzDEaeY7ZfevtV+vxao9BSUXu+GehHe9jO47fDZ8VKz4n6mNd/1lZrdeUteQ+unuFnKOZYzTyHLP71tuv1uPteK2txOr1t+r43fBZ4c9J95+joWOsOPcRs3Ihon1CiF2dXgfT/fBnpXOsJUuB6Txf7fQCmBUDf1Y6BFsKDMMwjAZbCgzDMIwGiwLDMAyjwaLAMAzDaHR88hrDAAARXQjgI1CKln4shPhKh5fEdClE9BYo81d6APyLEOK+Di9pVcGWArNsiOjrRDRNRM+VbX8tER0moqNE9PFaxxBCPC+E+BCAmwFca+V6mc7Ros/KHUKI3wTwIQDvsnK9axHOPmKWDRG9DEAMwG1CiBep2+wAjgC4EcAElEl874YycOkvyw7xfiHENBHdBOC3AXxDCPHNdq2faR+t+qyoz/s8gP8QQjzRpuWvCVgUmJZARGMAfqD7ol8N4NNCiNeov38CAIQQ5V9yo2P9UAjxButWy3SS5X5WiIgAfBbA/UKIB9qx5rUExxQYq9gA4Izu9wkAL662MxFdD+BtANwom9bHrHoa+qwA+DCAGwD0EtE2IcQ/Wbm4tQaLAtMVCCEeBPBgh5fBrACEEF8A8IVOr2O1woFmxirOAtio+31U3cYw5fBnpYtgUWCsYi+A7UQ0TkQuALcAuKvDa2K6E/6sdBEsCsyyIaL/BPAogAuIaIKIfkMIkQNwK4B7ATwP4NtCiAOdXCfTefiz0v1w9hHDMAyjwZYCwzAMo8GiwDAMw2iwKDAMwzAaLAoMwzCMBosCwzAMo8GiwDAMw2iwKDCWQ0SxNpzjpnotly045/VEdE0Tz7uCiP5F/fl9RPTF1q+ucYhorLyltcE+g0T0o3atiWk/LArMikFtsWyIEOIuIcRnLThnrf5g1wNoWBQA/DFWaO8eIcQMgPNExDMvViksCkxbIaI/IKK9RPQMEf2ZbvsdRLSfiA4Q0Qd122NE9HkiehrA1UR0koj+jIieIKJniWinup92x01E/0pEXyCiXxDRcSJ6h7rdRkRfJqJDRHQ/Ed0jHytb44NE9PdEtA/AR4joTUT0SyJ6kogeIKJhtf3zhwB8lIieIqKXqnfR31P/vr1GF04iCgK4VAjxtMFjY0T0E/W1+TERbVK3byWix9S/98+NLC8i8hPRD4noaSJ6jojepW7frb4OTxPR40QUVM/zsPoaPmFk7RCRnYg+p3uvfkv38B0AftXwDWZWPkII/sf/LP0HIKb+/2oAXwVAUG5IfgDgZepjYfV/L4DnAPSrvwsAN+uOdRLAh9Wf/weAr6k/vw/AF9Wf/xXAd9RzXATgqLr9HVDactsAjABYAPAOg/U+CODLut9DKFb/fwDA59WfPw3gf+r2+yaA69SfNwF43uDYrwDwPd3v+nXfDeC96s/vB3CH+vMPALxb/flD8vUsO+7bAfxf3e+9AFwAjgPYrW7rgdIZ2QfAo27bDmCf+vMYgOfUnz8I4JPqz24A+wCMq79vAPBspz9X/M+af9w6m2knr1b/Pan+HoByUXoIwO8S0VvV7RvV7XMA8gC+V3ac/1L/3w9lBoMRdwghCgAOEtGwuu06AN9Rt08S0U9rrPVbup9HAXyLiNZBudCeqPKcGwBcpMyAAQD0EFFACKG/s18HYKbK86/W/T3fAPDXuu1vUX/+JoC/MXjuswA+T0R/BWWAzcNEdAmA80KIvQAghFgCFKsCwBeJ6HIor+8Og+O9GsClOkuqF8p7cgLANID1Vf4GZoXDosC0EwLwl0KIfy7ZqAzYuQHA1UKIBBE9CMCjPpwSQuTLjpNW/8+j+mc4rfuZquxTi7ju538E8LdCiLvUtX66ynNsAF4ihEjVOG4Sxb+tZQghjhDRlQBeD+DPiejHAL5fZfePApgCcBmUNRutl6BYZPcaPOaB8ncwqxCOKTDt5F4A7yeiAAAQ0QYiGoJyF7qgCsJOAC+x6PyPAHi7GlsYhhIoNkMviv3936vbHgUQ1P1+H5SpYAAA9U68nOcBbKtynl9AaRsNKD77h9WfH4PiHoLu8RKIaD2AhBDi3wF8DsCVAA4DWEdEu9V9gmrgvBeKBVEA8B4os5DLuRfAbxORU33uDtXCABTLomaWErNyYVFg2oYQ4j4o7o9HiehZAN+FclH9EQAHET0PZfbuYxYt4XtQRj0eBPDvAJ4AEDHxvE8D+A4R7Qcwq9t+N4C3ykAzgN8FsEsNzB6E4v8vQQhxCMoYyWD5Y1AE5deJ6BkoF+uPqNt/D8DH1O3bqqz5EgCPE9FTAP4UwJ8LITIA3gXgH9VA/f1Q7vK/DOC96radKLWKJF+D8jo9oaap/jOKVtkrAPzQ4DnMKoBbZzNrCunjJ6J+AI8DuFYIMdnmNXwUQFQI8TWT+/sAJIUQgohugRJ0frOli6y9nocAvFkIsdCpNTDWwTEFZq3xAyLqgxIw/t/tFgSVrwB4ZwP7XwUlMEwAFqFkJnUEIhqEEl9hQVilsKXAMAzDaHBMgWEYhtFgUWAYhmE0WBQYhmEYDRYFhmEYRoNFgWEYhtFgUWAYhmE0/n+vFVVq4UL2mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder.plot_loss_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.set_value(model.optimizer.lr, 0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = tf.keras.callbacks.ModelCheckpoint('model-{epoch:03d}-{val_acc:03f}.h5', verbose=1, monitor='val_acc',save_best_only=True, mode='max')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00020: val_acc did not improve from 0.76690\n"
     ]
    }
   ],
   "source": [
    "out = model.fit([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY,\n",
    "                    batch_size=256,\n",
    "                    epochs=20,\n",
    "                    verbose=0,\n",
    "                    shuffle=True,\n",
    "                    validation_data=([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY),callbacks=[live(),checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper patameter search 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bert_trainable' :[False],\n",
    "    'text_no_hidden_layer':(0,2,2),\n",
    "    'text_hidden_neurons':[768,400],\n",
    "    'dropout':(0.3,0.6,3),\n",
    "    'repr_size':[32],\n",
    "    'vis_no_hidden_layer':(0,2,2),\n",
    "    'vis_hidden_neurons':[4096,2742],\n",
    "    'final_no_hidden_layer':(0,2,2),\n",
    "    'final_hidden_neurons':[64,35],\n",
    "    'optimizer':['adam','rmsprop'],\n",
    "    'batch_size':[256],\n",
    "    'epochs':[3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = ta.Scan([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, params=params,\n",
    "            model=news_model,\n",
    "            dataset_name='chiness_fake_news',\n",
    "            experiment_no='1',\n",
    "            x_val=[test_input_ids, test_input_masks, test_segment_ids,test_imagesX],\n",
    "            y_val=testY,\n",
    "            grid_downsample=.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets look at Hyper parameter experiment 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ta.Reporting(h)\n",
    "# r = ta.Reporting('chiness_fake_news_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8309608540925267"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.high('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x756 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert_trainable\n",
      "text_no_hidden_layer\n",
      "text_hidden_neurons\n",
      "dropout\n",
      "repr_size\n",
      "vis_no_hidden_layer\n",
      "vis_hidden_neurons\n",
      "final_no_hidden_layer\n",
      "final_hidden_neurons\n",
      "optimizer\n",
      "batch_size\n",
      "epochs\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAHPCAYAAABUeszdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3X9s3Vdh9/FP7Di2Yydp0mKDDYQV09J2pC4laVrQ1iEYBRGylYkmHUxlaAubeFR1Gs1oKyoEUiN1yKvWlEnbYFpSEpONbSLbhIrUKRpSnVBCsrGM1U67YZJikirND9nXybWfP3jmJ5bT2GncXLfn9fqLe77nXs6RoqO3br/+3nnj4+PjAQCAQtXVegEAAFBLghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIYRZ94QtfqPUSAIALJIhhFr3wwgu1XgIAcIHmz2TSqVOnsnXr1hw4cCCtra1Zu3ZtVq5cOWXe6dOns2PHjuzbty/VajVXXnll7rzzzlx22WVJkq997Wv50Y9+lNHR0SxevDjvf//78+53v3t2dwQAABdgRkHc29ub+vr6bNq0KYODg3nsscfS2dmZjo6OSfOefPLJPPvss7n//vvT3Nycxx9/PL29vdmwYUOS5AMf+EA+/vGPp6GhIc8//3z+5E/+JG9605vy5je/efZ3BgAAMzDtLROVSiV79+7NmjVr0tTUlK6urqxYsSK7d++eMvfo0aO55pprsnjx4jQ0NOTGG2/M4cOHJ653dHSkoaEhSTJv3rwkyc9+9rPZ2gsAAFywaYN4aGgodXV1aW9vnxjr7OzMoUOHpsy95ZZbcvDgwRw7diyjo6PZs2dPrrvuuklztm3blrvvvjtf+MIXsmTJkinXAYDXDn9szKvBtLdMVCqVNDc3Txprbm5OpVKZMretrS1Lly7Nfffdl7q6unR0dOSOO+6YNGf9+vW54447cvDgwTzzzDMT3xhfjBMnTlz0Z8BsqFar/j0CnOVnP/uZc5E5YdGiRS95bdogbmxszPDw8KSxkZGRNDY2Tpm7ffv2nDlzJg8//HAWLFiQJ554Ips3b8699947aV5dXV26urqye/fu7Nq1K7/yK78y072c0/k2CJdSfX29f48AZ3Eu8mow7S0TbW1tGRsby9DQ0MTY4ODglD+o+9/x1atXp6WlJQ0NDbn11lvz3HPP5eTJk+f87LGxMfcQAwBQU9MGcWNjY7q7u7Nz585UKpUMDAxk//79WbVq1ZS5y5cvT19fX4aHh1OtVrNr164sWbIkra2tOXHiRL73ve9lZGQkY2Nj+Y//+I9873vfy9vf/vZXZGMAADATM3rs2rp167Jly5Zs3LgxLS0tWb9+fTo6OtLf35/Nmzenp6cnSXL77bdnx44defDBB1OtVtPR0THxyLUk2bVrV7Zt25bx8fEsW7Ysv/Ebv5EVK1a8MjsDAIAZmDc+Pj5e60XAa8Xdd9+dRx55pNbLAJgznIu8GvjpZgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAo2vyZTDp16lS2bt2aAwcOpLW1NWvXrs3KlSunzDt9+nR27NiRffv2pVqt5sorr8ydd96Zyy67LKdPn8727dvzox/9KKdOncrrXve6rF27Ntddd92sbwoAAGZqRt8Q9/b2pr6+Pps2bcpdd92Vbdu25dChQ1PmPfnkk3n22Wdz//3356GHHsrChQvT29ubJBkbG8vSpUtzzz335Mtf/nLWrFmTv/iLv8jRo0dnd0cAAHABpg3iSqWSvXv3Zs2aNWlqakpXV1dWrFiR3bt3T5l79OjRXHPNNVm8eHEaGhpy44035vDhw0mSxsbGfPjDH87ll1+eurq6vOMd78jll1+e//mf/5n9XQEAwAxNG8RDQ0Opq6tLe3v7xFhnZ+c5vyG+5ZZbcvDgwRw7diyjo6PZs2fPS94Scfz48QwNDeUNb3jDRSwfAAAuzrT3EFcqlTQ3N08aa25uTqVSmTK3ra0tS5cuzX333Ze6urp0dHTkjjvumDKvWq3ma1/7WlavXp3Xv/71F7H8nztx4sRFfwbMhmq16t8jwFmci8wVixYteslr0wZxY2NjhoeHJ42NjIyksbFxytzt27fnzJkzefjhh7NgwYI88cQT2bx5c+69996JOWNjY/mrv/qrzJ8//5yx/HKcb4NwKdXX1/v3CHAW5yKvBtPeMtHW1paxsbEMDQ1NjA0ODqajo2PK3MHBwaxevTotLS1paGjIrbfemueeey4nT55MkoyPj2fr1q05fvx4fud3fif19fWzuBUAALhw0wZxY2Njuru7s3PnzlQqlQwMDGT//v1ZtWrVlLnLly9PX19fhoeHU61Ws2vXrixZsiStra1Jkm3btuX555/P7/3e72XBggWzvxsAALhAM3oO8bp167Jly5Zs3LgxLS0tWb9+fTo6OtLf35/Nmzenp6cnSXL77bdnx44defDBB1OtVtPR0ZENGzYk+fkTKP71X/818+fPz+c+97mJz16/fv054xoAAC6FeePj4+O1XgS8Vtx999155JFHar0MgDnDucirwYy+IQaAC/V/7vlsfnrkhVovgxp78chP87FPfKrWy2AOaL9iWf605+FaL+OcBDEAr4ifHnkh/7343bVeBrW2ODlW6zUwNxz5bq1X8JJm9NPNAADwWiWIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomp9ungX/557P5qdHXqj1MpgDXjzy03zsE5+q9TKosfYrluVPex6u9TIAmCFBPAt+euSF/Pfid9d6GcwFi5NjtV4DtXfku7VeAQAXwC0TAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARZs/k0mnTp3K1q1bc+DAgbS2tmbt2rVZuXLllHmnT5/Ojh07sm/fvlSr1Vx55ZW58847c9lllyVJ/uVf/iVPPfVUDh06lHe96135rd/6rdndDQAAXKAZfUPc29ub+vr6bNq0KXfddVe2bduWQ4cOTZn35JNP5tlnn83999+fhx56KAsXLkxvb+/E9SVLluS2227LzTffPHs7AACAizBtEFcqlezduzdr1qxJU1NTurq6smLFiuzevXvK3KNHj+aaa67J4sWL09DQkBtvvDGHDx+euH7DDTeku7s7LS0ts7sLAAB4maYN4qGhodTV1aW9vX1irLOz85zfEN9yyy05ePBgjh07ltHR0ezZsyfXXXfd7K4YAABm0bT3EFcqlTQ3N08aa25uTqVSmTK3ra0tS5cuzX333Ze6urp0dHTkjjvumL3VvoQTJ0684v8f5zO/vj6NddWargGYO+bX19f8XJoLnI3A2Wp9Ni5atOglr00bxI2NjRkeHp40NjIyksbGxilzt2/fnjNnzuThhx/OggUL8sQTT2Tz5s259957X8ayZ+58G7wUzlSrqYzV13QNwNxxplqt+bk0FzgbgbPN5bNx2lsm2traMjY2lqGhoYmxwcHBdHR0TJk7ODiY1atXp6WlJQ0NDbn11lvz3HPP5eTJk7O7agAAmCXTBnFjY2O6u7uzc+fOVCqVDAwMZP/+/Vm1atWUucuXL09fX1+Gh4dTrVaza9euLFmyJK2trUmSarWa06dPZ2xsLGNjYzl9+nSqVf85DQCA2pnRc4jXrVuXLVu2ZOPGjWlpacn69evT0dGR/v7+bN68OT09PUmS22+/PTt27MiDDz6YarWajo6ObNiwYeJz/vmf/zn/9E//NPF69+7d+dCHPpQPf/jDs7wtAACYmXnj4+PjtV7Eq93HPvGp/Pfid9d6GcAcsfz4d/ONLX9Z62XUnLMRONtcPhv9dDMAAEUTxAAAFE0QAwBQNEEMAEDRZvSUCQC4UC8e+WkWHP12rZcBzBEvjp+q9RJekiAG4BWx5Ir2HPOUCeD/ecPx79Z6CS/JLRMAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0ebXegGvBS8e+WkWHP12rZcBzBEvjp+q9RIAuACCeBYsuaI9xxa/u9bLAOaINxz/bq2XAMAFcMsEAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAULT5M5l06tSpbN26NQcOHEhra2vWrl2blStXTpl3+vTp7NixI/v27Uu1Ws2VV16ZO++8M5dddtkFfQ4AAFwqM/qGuLe3N/X19dm0aVPuuuuubNu2LYcOHZoy78knn8yzzz6b+++/Pw899FAWLlyY3t7eC/4cAAC4VKYN4kqlkr1792bNmjVpampKV1dXVqxYkd27d0+Ze/To0VxzzTVZvHhxGhoacuONN+bw4cMX/DkAAHCpTBvEQ0NDqaurS3t7+8RYZ2fnOb/ZveWWW3Lw4MEcO3Yso6Oj2bNnT6677roL/hwAALhUpr2HuFKppLm5edJYc3NzKpXKlLltbW1ZunRp7rvvvtTV1aWjoyN33HHHBX/OhTpx4sRFf8bFmF9fn8a6ak3XAMwd8+vra34uzQXORuBstT4bFy1a9JLXpg3ixsbGDA8PTxobGRlJY2PjlLnbt2/PmTNn8vDDD2fBggV54oknsnnz5tx7770X9DkX6nwbvBTOVKupjNXXdA3A3HGmWq35uTQXOBuBs83ls3HaWyba2toyNjaWoaGhibHBwcF0dHRMmTs4OJjVq1enpaUlDQ0NufXWW/Pcc8/l5MmTF/Q5AABwqUwbxI2Njenu7s7OnTtTqVQyMDCQ/fv3Z9WqVVPmLl++PH19fRkeHk61Ws2uXbuyZMmStLa2XtDnAADApTKjx66tW7cuo6Oj2bhxY7761a9m/fr16ejoSH9/f+65556JebfffnsaGhry4IMP5t57780Pf/jDbNiwYdrPAQCAWpnRD3O0tLTk05/+9JTxrq6u9PT0TLxubW3NJz/5yQv+HAAAqBU/3QwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARZs/k0mnTp3K1q1bc+DAgbS2tmbt2rVZuXLllHmPPvpoBgYGJl6fOXMm7e3teeCBB5IkAwMD+Zu/+Zs8//zzufzyy7Nu3bp0dXXN0lYAAODCzSiIe3t7U19fn02bNmVwcDCPPfZYOjs709HRMWneZz7zmUmve3p6cvXVVyf5eVT/2Z/9WdavX5/u7u7s2bMnX/nKV/LFL34xCxcunKXtAADAhZn2lolKpZK9e/dmzZo1aWpqSldXV1asWJHdu3ef931Hjx5Nf39/brrppiTJwYMHs3jx4rzzne9MXV1dbrrppixatCh79+6dnZ0AAMDLMG0QDw0Npa6uLu3t7RNjnZ2dOXTo0Hnf19fXl66urlx++eUTY+Pj45PmjI+P5/Dhwxe6ZgAAmDXT3jJRqVTS3Nw8aay5uTmVSuW87+vr68ttt9028foXfuEX8uKLL2bPnj155zvfmT179uTIkSMZHR19mUv//06cOHHRn3Ex5tfXp7GuWtM1AHPH/Pr6mp9Lc4GzEThbrc/GRYsWveS1aYO4sbExw8PDk8ZGRkbS2Nj4ku/p7+/P8ePHc8MNN0yMtba2ZsOGDfnmN7+Z3t7eXHvttbn66qtz2WWXzWQP53W+DV4KZ6rVVMbqa7oGYO44U63W/FyaC5yNwNnm8tk4bRC3tbVlbGwsQ0NDaWtrS5IMDg5O+YO6s/X19eX6669PU1PTpPGrrroqf/RHf5QkqVar+fznP5/3ve99F7N+AAC4KNPeQ9zY2Jju7u7s3LkzlUolAwMD2b9/f1atWnXO+aOjo3n66adz8803T7n24x//ONVqNcPDw/nmN7+ZpUuX5tprr734XQAAwMs0o8eurVu3Llu2bMnGjRvT0tKS9evXp6OjI/39/dm8eXN6enom5u7bty8LFy7MVVddNeVznnjiifz7v/97kuTaa6/Nhg0bZmkbAADw8swoiFtaWvLpT396ynhXV9ekGE6SlStXnvNHO5Lkt3/7t1/GEgEA4JXjp5sBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAogliAACKJogBACiaIAYAoGiCGACAos2fyaRTp05l69atOXDgQFpbW7N27dqsXLlyyrxHH300AwMDE6/PnDmT9vb2PPDAA0mSH//4x/nGN76Rn/zkJ2lqasp73vOefOhDH5qlrQAAwIWbURD39vamvr4+mzZtyuDgYB577LF0dnamo6Nj0rzPfOYzk1739PTk6quvnnj9ta99Lddff33uueeeHD16NF/+8pfzxje+MStWrJiFrQAAwIWb9paJSqWSvXv3Zs2aNWlqakpXV1dWrFiR3bt3n/d9R48eTX9/f2666aZJY6tWrUpdXV1e97rX5a1vfWsOHTp08bsAAICXadpviIeGhlJXV5f29vaJsc7OzjzzzDPnfV9fX1+6urpy+eWXT4y9973vTV9fX9asWZMjR47k2Wefzfvf//6LWP7PnThx4qI/42LMr69PY121pmsA5o759fU1P5fmAmcjcLZan42LFi16yWvTBnGlUklzc/Oksebm5lQqlfO+r6+vL7fddtuksV/8xV/MX//1X+c73/lOxsbG8qEPfShvectbplvCtM63wUvhTLWaylh9TdcAzB1nqtWan0tzgbMRONtcPhunDeLGxsYMDw9PGhsZGUljY+NLvqe/vz/Hjx/PDTfcMDF26tSpbN68OR/72MeycuXKHD9+PH/+53+eRYsW5Zd/+ZcvYgsAAPDyTXsPcVtbW8bGxjI0NDQxNjg4OOUP6s7W19eX66+/Pk1NTRNjR44cybx587J69erU19dn6dKlede73pUf/vCHF7kFAAB4+aYN4sbGxnR3d2fnzp2pVCoZGBjI/v37s2rVqnPOHx0dzdNPP52bb7550nhbW1uSZM+ePRkbG8uLL76Yp59+Op2dnbOwDQAAeHlm9Ni1devWZcuWLdm4cWNaWlqyfv36dHR0pL+/P5s3b05PT8/E3H379mXhwoW56qqrJn1Gc3Nzfvd3fzd/93d/l23btmXBggV5xzvekQ9+8IOzuyMAALgA88bHx8drvYhXu4994lP578XvrvUygDli+fHv5htb/rLWy6g5ZyNwtrl8NvrpZgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBo82u9gNeC9iuWJUe+W+tlMAe8eOSnWXJFe62XQY21X7Gs1ksA4AII4lnwpz0P13oJzBF33313HnnkkVovAwC4AG6ZAACgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICieewaAK8Iz2gn8Xx2/r+5/Ix2QQzAK8Iz2kk8n51XB7dMAABQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQtPkzmXTq1Kls3bo1Bw4cSGtra9auXZuVK1dOmffoo49mYGBg4vWZM2fS3t6eBx54IC+88EK++MUvTppfqVRy++23533ve99FbgMAAF6eGQVxb29v6uvrs2nTpgwODuaxxx5LZ2dnOjo6Js37zGc+M+l1T09Prr766iTJsmXL0tPTM3HtyJEjefDBB3PDDTdc7B4AAOBlm/aWiUqlkr1792bNmjVpampKV1dXVqxYkd27d5/3fUePHk1/f39uuummc17v6+tLV1dXLr/88pe3cgAAmAXTfkM8NDSUurq6tLe3T4x1dnbmmWeeOe/7zhe84+Pj6evrywc/+MGXseSpTpw4MSufAxerWq369whwFucic8WiRYte8tq0QVypVNLc3DxprLm5OZVK5bzv6+vry2233XbOawMDAzlx4sSs3S5xvg3CpVRfX+/fI8BZnIu8Gkx7y0RjY2OGh4cnjY2MjKSxsfEl39Pf35/jx4+/ZPA+9dRT6e7uTlNT0wUuFwAAZte0QdzW1paxsbEMDQ1NjA0ODk75g7qz9fX15frrrz9n8I6Ojub73/9+Vq9e/TKXDAAAs2dG3xB3d3dn586dqVQqGRgYyP79+7Nq1apzzh8dHc3TTz+dm2+++ZzX9+3bl4ULF+aqq666uJUDAMAsmNEPc6xbty6jo6PZuHFjvvrVr2b9+vXp6OhIf39/7rnnnklzpwvep556KjfddFPmzZt38asHAICLNG98fHy81ouA14q77747jzzySK2XATBnOBd5NfDTzQAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxAAAFE0QAwBQNEEMAEDRBDEAAEUTxDCLli1bVuslAMwpzkVeDeaNj4+P13oRAABQK74hBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaPNrvYDZMG/evFovAQCAOW58fPyc46+JIH6pzQEAwHTcMgEAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARXtNPHYNLpWhoaF86Utfyg033JBPfvKTSZI9e/bkH/7hH3Ly5Mm8/e1vzyc+8Ym0tLQkSU6dOpWtW7fmwIEDaW1tzdq1a7Ny5cpabgFgVtxzzz2TXo+OjuaXfumXcscdd0y8/tu//dt8//vfT7VazRvf+Mb8wR/8QZLk9OnT2bFjR/bt25dqtZorr7wyd955Zy677LJLvg9IBDFckO3bt2f58uUTrw8dOpSvf/3r+f3f//286U1vyte//vVs3749n/rUp5Ikvb29qa+vz6ZNmzI4OJjHHnssnZ2d6ejoqNUWAGZFT0/PxP8eGRnJ5z73ubzzne+cGHv88cczNjaWz3/+82lpacng4ODEtSeffDLPPvts7r///jQ3N+fxxx9Pb29vNmzYcEn3AP/LLRMwQ9/73veycOHCXH311RNje/bsyTve8Y687W1vS1NTU9asWZMf/OAHGRkZSaVSyd69e7NmzZo0NTWlq6srK1asyO7du2u4C4DZ94Mf/CCtra3p6upKkjz//PP5t3/7t9x5551ZtGhR6urq8uY3v3li/tGjR3PNNddk8eLFaWhoyI033pjDhw/XavkgiGEmhoeHs3Pnznz0ox+dNH748OG88Y1vnHj9ute9LvPnz8/Q0FCGhoZSV1eX9vb2ieudnZ05dOjQJVs3wKXw1FNP5aabbsq8efOSJM8991yWLVuWf/zHf8xnP/vZfOlLX8revXsn5t9yyy05ePBgjh07ltHR0ezZsyfXXXddrZYPbpmAmfjWt76VW265JUuXLp00XqlU0tTUNGmsqakpIyMjqaurS3Nz86Rrzc3NqVQqr/h6AS6Vo0eP5plnnsnHP/7xibFjx47l0KFD6e7uzkMPPZSDBw/mK1/5Sl7/+tfnDW94Q9ra2rJ06dLcd999qaurS0dHx8S9x1ALviGGafz4xz/Oj370o7z3ve+dcq2xsTEjIyOTxkZGRtLU1JTGxsYMDw+Jp6RPAAACHElEQVRPudbY2PiKrhfgUtq9e3fe+ta35oorrpgYa2hoSH19fT74wQ9m/vz5ueqqq/K2t70tBw4cSPLzv8c4c+ZMHn744fT09KS7uzubN2+u1RbAN8QwnWeeeSZHjx7NAw88kOTn3wqPjY3loYceyrXXXpuf/OQnE3OPHDmSM2fOpK2tLfPmzcvY2FiGhobS1taWJBkcHPQHdcBrSl9fX371V3910lhnZ+eUef97O0Xy87PwIx/5yMQTeW699dbs3LkzJ0+eTGtr6yu7YDgHQQzTeM973pMbb7xx4vV3vvOdvPDCC1m3bl1OnDiRP/7jP05/f3/e9KY35Vvf+la6u7snbqPo7u7Ozp0785u/+ZsZHBzM/v3784d/+Ie12grArBoYGMixY8cmPV0iSd72trdl2bJl+fa3v50PfOADee655/Jf//Vf+fVf//UkyfLly9PX15errroqCxYsyK5du7JkyRIxTM3MGx8fH6/1IuDVZOfOnfnZz3426TnEf//3f59Tp06d8znEW7ZsyX/+53+mpaUlv/Zrv+Y5xMBrxte//vWMjo7mrrvumnLt0KFDefzxx/OTn/wky5Yty0c+8pF0d3cnSU6ePJkdO3bkwIEDqVar6ejoyEc/+tG85S1vubQbgP9HEAMAUDR/VAcAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNH+L2OXo9lFyiCoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAHPCAYAAABUeszdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHuVJREFUeJzt3W9s3eV99/FP7DjHjp2EkGKvdtsw5vEvI4RSh4R2GpuKRhFRWJEIQfvDNHWoEhXiwQ0T0E1TJxGJVRm6MZvUrlRNpsRkaqUp2nSPSUyRquGkLErUziBsQls3AS+uIInlHCfHvh9M813fDrGTeNjher2eoHP9vueX63pivXX42WfRxMTERAAAoFB1870BAACYT4IYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAE+gv78z/98vrcAcNkQxAAfQT//+c/newsAl43FsxkaGRnJzp0709fXl5aWlmzevDldXV3T5s6cOZM9e/bk0KFDqdVqueaaa/Lggw/miiuuSJK8+OKLeeONNzI2Npbly5fnzjvvzGc/+9m5PREAAFyAWQVxT09P6uvrs23btgwODuaFF15IR0dH2tvbp8y98sorOXLkSJ566qk0NTXl7/7u79LT05OHH344SfLbv/3b+d3f/d00NDTknXfeyV/91V/lk5/8ZD71qU/N/ckAAGAWZnxkolqt5uDBg9m0aVMaGxvT2dmZtWvXZv/+/dNmh4eHc8MNN2T58uVpaGjIrbfemmPHjk1eb29vT0NDQ5Jk0aJFSZL//M//nKuzAADABZvxE+KhoaHU1dWlra1tcq2joyNvvvnmtNnbb789e/bsyXvvvZelS5fmwIEDWbNmzZSZXbt25dVXX82ZM2fyyU9+ctp1AAD4MM0YxNVqNU1NTVPWmpqaUq1Wp822trZm5cqVefLJJ1NXV5f29vZs2bJlyszWrVuzZcuWvPXWW3nzzTcnPzG+FCdPnrzkewB8lNRqNT8bAX7BsmXLPvDajEFcqVQyOjo6Ze306dOpVCrTZnfv3p2zZ8/m2WefzZIlS/Lyyy+nu7s7jz/++JS5urq6dHZ2Zv/+/dm3b19+8zd/c7ZnOafzHRCgRPX19X42AszSjM8Qt7a2Znx8PENDQ5Nrg4OD036h7r/XN2zYkObm5jQ0NOSOO+7I22+/nVOnTp3z3uPj454hBgBgXs0YxJVKJevWrcvevXtTrVYzMDCQw4cPZ/369dNmV69end7e3oyOjqZWq2Xfvn1ZsWJFWlpacvLkyfzgBz/I6dOnMz4+nv/4j//ID37wg1x//fX/IwcDAIDZWDQxMTEx09DIyEh27NiR119/Pc3Nzbn33nvT1dWV/v7+dHd3Z/v27UmSU6dOZc+ePenr60utVkt7e3vuu+++XH311Tl58mS+8Y1v5Gc/+1kmJiZy5ZVX5o477sjnPve5//FDApTm0UcfzXPPPTff2wC4LMwqiAG4vAhigNnz1c0AABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAULTFsxkaGRnJzp0709fXl5aWlmzevDldXV3T5s6cOZM9e/bk0KFDqdVqueaaa/Lggw/miiuuyJkzZ7J79+688cYbGRkZyVVXXZXNmzdnzZo1c34oAACYrVl9QtzT05P6+vps27YtDz30UHbt2pWjR49Om3vllVdy5MiRPPXUU3nmmWeydOnS9PT0JEnGx8ezcuXKPPbYY/n617+eTZs25Zvf/GaGh4fn9kQAAHABZgziarWagwcPZtOmTWlsbExnZ2fWrl2b/fv3T5sdHh7ODTfckOXLl6ehoSG33nprjh07liSpVCq55557smrVqtTV1eWmm27KqlWr8pOf/GTuTwUAALM0YxAPDQ2lrq4ubW1tk2sdHR3n/IT49ttvz1tvvZX33nsvY2NjOXDgwAc+EnHixIkMDQ3l4x//+CVsHwAALs2MzxBXq9U0NTVNWWtqakq1Wp0229rampUrV+bJJ59MXV1d2tvbs2XLlmlztVotL774YjZs2JBf+qVfuoTt/5eTJ09e8j0APkpqtZqfjQC/YNmyZR94bcYgrlQqGR0dnbJ2+vTpVCqVabO7d+/O2bNn8+yzz2bJkiV5+eWX093dnccff3xyZnx8PN/+9rezePHic8byxTjfAQFKVF9f72cjwCzN+MhEa2trxsfHMzQ0NLk2ODiY9vb2abODg4PZsGFDmpub09DQkDvuuCNvv/12Tp06lSSZmJjIzp07c+LEiXzpS19KfX39HB4FAAAu3IxBXKlUsm7duuzduzfVajUDAwM5fPhw1q9fP2129erV6e3tzejoaGq1Wvbt25cVK1akpaUlSbJr16688847+fKXv5wlS5bM/WkAAOACLZqYmJiYaWhkZCQ7duzI66+/nubm5tx7773p6upKf39/uru7s3379iTJqVOnsmfPnvT19aVWq6W9vT333Xdfrr766gwPD+erX/1qFi9ePOWT4a1bt54zrgG4eI8++miee+65+d4GwGVhVkEMwOVFEAPMniD+iPjKY/8r7x7/+XxvA1gg3j/+blZ8rG3mQaAIbR+7Mv97+7PzvY0Fa1Zf3czC9+7xn+fHyz8739sAForlyXvzvQdg4Tj+/fnewYI2q69uBgCAjypBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRt8WyGRkZGsnPnzvT19aWlpSWbN29OV1fXtLkzZ85kz549OXToUGq1Wq655po8+OCDueKKK5Ik//qv/5pXX301R48ezWc+85n8/u///tyeBgAALtCsPiHu6elJfX19tm3bloceeii7du3K0aNHp8298sorOXLkSJ566qk888wzWbp0aXp6eiavr1ixInfddVc2btw4dycAAIBLMGMQV6vVHDx4MJs2bUpjY2M6Ozuzdu3a7N+/f9rs8PBwbrjhhixfvjwNDQ259dZbc+zYscnrt9xyS9atW5fm5ua5PQUAAFykGYN4aGgodXV1aWtrm1zr6Og45yfEt99+e95666289957GRsby4EDB7JmzZq53TEAAMyhGZ8hrlaraWpqmrLW1NSUarU6bba1tTUrV67Mk08+mbq6urS3t2fLli1zt9sPcPLkyf/xf2OhW1xfn0pdbb63AQAsQIvr64vvpWXLln3gtRmDuFKpZHR0dMra6dOnU6lUps3u3r07Z8+ezbPPPpslS5bk5ZdfTnd3dx5//PGL2Pbsne+ApThbq6U6Xj/f2wAAFqCztZpeOo8ZH5lobW3N+Ph4hoaGJtcGBwfT3t4+bXZwcDAbNmxIc3NzGhoacscdd+Ttt9/OqVOn5nbXAAAwR2YM4kqlknXr1mXv3r2pVqsZGBjI4cOHs379+mmzq1evTm9vb0ZHR1Or1bJv376sWLEiLS0tSZJarZYzZ85kfHw84+PjOXPmTGo1/5sfAID5s2hiYmJipqGRkZHs2LEjr7/+epqbm3Pvvfemq6sr/f396e7uzvbt25Mkp06dyp49e9LX15darZb29vbcd999ufrqq5Mke/fuzT/+4z9Ouffdd9+de+65Z+5PVpj7f++P8uPln53vbQAAC9DqE9/PSzv+dr63sWDNKohZ+AQxAPBBBPH5+epmAACKJogBACiaIAYAoGiCGACAos34xRxcHt4//m6WDP+f+d4GALAAvT8xMt9bWNAE8UfEio+15T1/ZQIAOIePn/j+fG9hQfPIBAAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEEMQAARRPEAAAUTRADAFA0QQwAQNEWz2ZoZGQkO3fuTF9fX1paWrJ58+Z0dXVNmztz5kz27NmTQ4cOpVar5ZprrsmDDz6YK6644oLuAwAAH5ZZfULc09OT+vr6bNu2LQ899FB27dqVo0ePTpt75ZVXcuTIkTz11FN55plnsnTp0vT09FzwfQAA4MMyYxBXq9UcPHgwmzZtSmNjYzo7O7N27drs379/2uzw8HBuuOGGLF++PA0NDbn11ltz7NixC74PAAB8WGYM4qGhodTV1aWtrW1yraOj45yf7N5+++1566238t5772VsbCwHDhzImjVrLvg+AADwYZnxGeJqtZqmpqYpa01NTalWq9NmW1tbs3Llyjz55JOpq6tLe3t7tmzZcsH3uVAnT5685Htc7hbX16dSV5vvbQAAC9Di+vrie2nZsmUfeG3GIK5UKhkdHZ2ydvr06VQqlWmzu3fvztmzZ/Pss89myZIlefnll9Pd3Z3HH3/8gu5zoc53wFKcrdVSHa+f720AAAvQ2VpNL53HjI9MtLa2Znx8PENDQ5Nrg4ODaW9vnzY7ODiYDRs2pLm5OQ0NDbnjjjvy9ttv59SpUxd0HwAA+LDMGMSVSiXr1q3L3r17U61WMzAwkMOHD2f9+vXTZlevXp3e3t6Mjo6mVqtl3759WbFiRVpaWi7oPgAA8GFZNDExMTHT0MjISHbs2JHXX389zc3Nuffee9PV1ZX+/v50d3dn+/btSZJTp05lz5496evrS61WS3t7e+67775cffXV570Pl+7+3/uj/Hj5Z+d7GwDAArT6xPfz0o6/ne9tLFizCmIWPkEMAHwQQXx+vroZAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKJogBgCgaIIYAICiCWIAAIq2eDZDIyMj2blzZ/r6+tLS0pLNmzenq6tr2tzzzz+fgYGByddnz55NW1tbnn766STJwMBA/v7v/z7vvPNOVq1alQceeCCdnZ1zdBQAALhwswrinp6e1NfXZ9u2bRkcHMwLL7yQjo6OtLe3T5l75JFHprzevn17rrvuuiT/FdV/8zd/k61bt2bdunU5cOBA/vqv/zpf+9rXsnTp0jk6DgAAXJgZH5moVqs5ePBgNm3alMbGxnR2dmbt2rXZv3//ed83PDyc/v7+3HbbbUmSt956K8uXL8+nP/3p1NXV5bbbbsuyZcty8ODBuTkJAABchBmDeGhoKHV1dWlra5tc6+joyNGjR8/7vt7e3nR2dmbVqlWTaxMTE1NmJiYmcuzYsQvdMwAAzJkZH5moVqtpamqastbU1JRqtXre9/X29uauu+6afP3Lv/zLef/993PgwIF8+tOfzoEDB3L8+PGMjY1d5Nb/n5MnT17yPS53i+vrU6mrzfc2AIAFaHF9ffG9tGzZsg+8NmMQVyqVjI6OTlk7ffp0KpXKB76nv78/J06cyC233DK51tLSkocffjjf/e5309PTkxtvvDHXXXddrrjiitmc4bzOd8BSnK3VUh2vn+9tAAAL0NlaTS+dx4xB3NramvHx8QwNDaW1tTVJMjg4OO0X6n5Rb29vbr755jQ2Nk5Zv/baa/Mnf/InSZJarZY//dM/zec///lL2T8AAFySGZ8hrlQqWbduXfbu3ZtqtZqBgYEcPnw469evP+f82NhYXnvttWzcuHHatZ/+9Kep1WoZHR3Nd7/73axcuTI33njjpZ8CAAAu0qz+7NoDDzyQHTt25Iknnkhzc3O2bt2a9vb29Pf3p7u7O9u3b5+cPXToUJYuXZprr7122n1efvnl/PCHP0yS3HjjjXn44Yfn6BgAAHBxFk38/3/6gcvS/b/3R/nx8s/O9zYAgAVo9Ynv56Udfzvf21iwfHUzAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAULTFsxkaGRnJzp0709fXl5aWlmzevDldXV3T5p5//vkMDAxMvj579mza2try9NNPJ0l++tOf5qWXXsrPfvazNDY25nOf+1zuvvvuOToKAABcuFkFcU9PT+rr67Nt27YMDg7mhRdeSEdHR9rb26fMPfLII1Neb9++Pdddd93k6xdffDE333xzHnvssQwPD+frX/96PvGJT2Tt2rVzcBQAALhwMz4yUa1Wc/DgwWzatCmNjY3p7OzM2rVrs3///vO+b3h4OP39/bntttumrK1fvz51dXW56qqr8iu/8is5evTopZ8CAAAu0oyfEA8NDaWuri5tbW2Tax0dHXnzzTfP+77e3t50dnZm1apVk2u/9Vu/ld7e3mzatCnHjx/PkSNHcuedd17C9v/LyZMnL/kel7vF9fWp1NXmexsAwAK0uL6++F5atmzZB16bMYir1WqampqmrDU1NaVarZ73fb29vbnrrrumrP3ar/1avvOd7+Rf/uVfMj4+nrvvvjtXX331TFuY0fkOWIqztVqq4/XzvQ0AYAE6W6vppfOYMYgrlUpGR0enrJ0+fTqVSuUD39Pf358TJ07klltumVwbGRlJd3d37r///nR1deXEiRP5xje+kWXLluU3fuM3LuEIAABw8WZ8hri1tTXj4+MZGhqaXBscHJz2C3W/qLe3NzfffHMaGxsn144fP55FixZlw4YNqa+vz8qVK/OZz3wmP/rRjy7xCAAAcPFmDOJKpZJ169Zl7969qVarGRgYyOHDh7N+/fpzzo+NjeW1117Lxo0bp6y3trYmSQ4cOJDx8fG8//77ee2119LR0TEHxwAAgIszqz+79sADD2THjh154okn0tzcnK1bt6a9vT39/f3p7u7O9u3bJ2cPHTqUpUuX5tprr51yj6ampvzxH/9xvve972XXrl1ZsmRJbrrppnzhC1+Y2xMBAMAFWDQxMTEx35vg0t3/e3+UHy//7HxvAwBYgFaf+H5e2vG3872NBctXNwMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0RbP9waYG20fuzI5/v353gawQLx//N2s+FjbfG8DWCDaPnblfG9hQVs0MTExMd+bAGBuPfroo3nuuefmexsAlwWPTAAAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0RbPZmhkZCQ7d+5MX19fWlpasnnz5nR1dU2be/755zMwMDD5+uzZs2lra8vTTz+dn//85/na1742Zb5areaLX/xiPv/5z1/iMQAA4OLMKoh7enpSX1+fbdu2ZXBwMC+88EI6OjrS3t4+Ze6RRx6Z8nr79u257rrrkiRXXnlltm/fPnnt+PHj+bM/+7Pccsstl3oGAAC4aDM+MlGtVnPw4MFs2rQpjY2N6ezszNq1a7N///7zvm94eDj9/f257bbbznm9t7c3nZ2dWbVq1cXtHAAA5sCMnxAPDQ2lrq4ubW1tk2sdHR158803z/u+8wXvxMREent784UvfOEitjzdyZMn5+Q+AB8VtVrNz0aAX7Bs2bIPvDZjEFer1TQ1NU1Za2pqSrVaPe/7ent7c9ddd53z2sDAQE6ePDlnj0uc74AAJaqvr/ezEWCWZnxkolKpZHR0dMra6dOnU6lUPvA9/f39OXHixAcG76uvvpp169alsbHxArcLAABza8Ygbm1tzfj4eIaGhibXBgcHp/1C3S/q7e3NzTfffM7gHRsby7//+79nw4YNF7llAACYO7P6hHjdunXZu3dvqtVqBgYGcvjw4axfv/6c82NjY3nttdeycePGc14/dOhQli5dmmuvvfbSdg4AAHNgVl/M8cADD2RsbCxPPPFEvvWtb2Xr1q1pb29Pf39/HnvssSmzMwXvq6++mttuuy2LFi269N0DAMAlWjQxMTEx35sAYG49+uijee655+Z7GwCXBV/dDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDPARdOWVV873FgAuG4smJiYm5nsTAAAwX3xCDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0QQxAABFE8QAABRNEAMAUDRBDABA0RbP9wbmwqJFi+Z7CwAALHATExPnXP9IBPEHHQ4AAGbikQkAAIomiAEAKJogBgCgaIIYAICiCWIAAIomiAEAKNpH4s+uAZTizJkz2b17d954442MjIzkqquuyubNm7NmzZoMDw/nq1/9aiqVyuT8nXfembvvvnvy9euvv57vfe97effdd7N06dLcd999ufXWW+fjKAALhiAGuIyMj49n5cqVeeyxx7Jy5cr86Ec/yje/+c08/fTTkzN/+Zd/mfr6+mnvPXbsWL71rW/lD/7gD3L99ddndHQ0o6OjH+b2ARYkQQxwGalUKrnnnnsmX990001ZtWpVfvKTn+RTn/rUed/7T//0T/n1X//1rFmzJknS0tKSlpaW/9H9AlwOBDHAZezEiRMZGhrKxz/+8cm1p59+OosWLcr111+fL37xi5PRe+TIkVx11VX5i7/4i5w6dSrXXXdd7r///jQ3N8/X9gEWhEUTvvcY4LJUq9Xy/PPP56qrrsqDDz6Y06dP5913380nPvGJjIyMpKenJ6dPn85XvvKVJMlXvvKVrFixYvK/3/nOd9LQ0JA//MM/nOeTAMwvf2UC4DI0Pj6eb3/721m8eHG2bNmSJGlsbMzq1atTX1+f5cuX5/77709fX19Onz6dJGloaMjGjRvT1taWxsbG3HXXXfnhD384n8cAWBAEMcBlZmJiIjt37syJEyfypS996Zy/QJckixYtmpxPko6Ojsk1AP4fQQxwmdm1a1feeeedfPnLX86SJUsm148cOZJ333034+PjOXXqVF566aX86q/+apqampIkGzduzL/927/l+PHjGRsbyz//8z/npptumq9jACwYniEGuIz8998aXrx48ZRPhrdu3ZpFixblH/7hH3Ly5Mk0Njbm+uuvz+/8zu9kxYoVk3N79+7Nvn37kiQ33nhj7r///ixduvRDPwfAQiKIAQAomkcmAAAomiAGAKBoghgAgKIJYgAAiiaIAQAomiAGAKBoghgAgKIJYgAAiiaIAQAo2v8FoLGWk5eWzvQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in params.keys():\n",
    "    print(k)\n",
    "    r.plot_box(y='val_acc',x=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c33f93080>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = r.data[['bert_trainable','text_no_hidden_layer','text_hidden_neurons','dropout','vis_no_hidden_layer','vis_hidden_neurons','final_no_hidden_layer','final_hidden_neurons','optimizer']]\n",
    "scaler = MinMaxScaler()\n",
    "y = scaler.fit_transform(r.data[['val_acc']])\n",
    "X['bert_trainable'] = pd.factorize(X['bert_trainable'])[0]\n",
    "X['optimizer'] = pd.factorize(X['optimizer'])[0]\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(max_depth=3,n_estimators=100)\n",
    "reg.fit(X,y)\n",
    "pd.Series(reg.feature_importances_,index=X.columns).\\\n",
    "sort_values(ascending=True).plot.barh(color='grey',title='Feature Importance of Hyperparameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_regs(x='acc',y='val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1344.48x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_bars(x='vis_no_hidden_layer',y='val_acc',hue='dropout',col='final_no_hidden_layer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.412353</td>\n",
       "      <td>0.815836</td>\n",
       "      <td>0.366472</td>\n",
       "      <td>0.852174</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.457777</td>\n",
       "      <td>0.781139</td>\n",
       "      <td>0.449915</td>\n",
       "      <td>0.801691</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.419918</td>\n",
       "      <td>0.784698</td>\n",
       "      <td>0.399365</td>\n",
       "      <td>0.820048</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.488341</td>\n",
       "      <td>0.775801</td>\n",
       "      <td>0.469698</td>\n",
       "      <td>0.792512</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.457858</td>\n",
       "      <td>0.798932</td>\n",
       "      <td>0.383433</td>\n",
       "      <td>0.842029</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0.415371</td>\n",
       "      <td>0.774911</td>\n",
       "      <td>0.412567</td>\n",
       "      <td>0.816908</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0.408569</td>\n",
       "      <td>0.803381</td>\n",
       "      <td>0.395928</td>\n",
       "      <td>0.830435</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0.428544</td>\n",
       "      <td>0.813167</td>\n",
       "      <td>0.375277</td>\n",
       "      <td>0.851208</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0.419095</td>\n",
       "      <td>0.814057</td>\n",
       "      <td>0.373091</td>\n",
       "      <td>0.849758</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0.379411</td>\n",
       "      <td>0.827402</td>\n",
       "      <td>0.356345</td>\n",
       "      <td>0.853382</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>3.757065</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>2.240332</td>\n",
       "      <td>0.826329</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>3.757065</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>2.953093</td>\n",
       "      <td>0.807971</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>3.757065</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>2.957779</td>\n",
       "      <td>0.807971</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>0.408706</td>\n",
       "      <td>0.815836</td>\n",
       "      <td>0.407310</td>\n",
       "      <td>0.824155</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>3.757065</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>2.925491</td>\n",
       "      <td>0.807971</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>3.754519</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>1.678414</td>\n",
       "      <td>0.810628</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>0.399836</td>\n",
       "      <td>0.824733</td>\n",
       "      <td>0.423743</td>\n",
       "      <td>0.827053</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>0.463528</td>\n",
       "      <td>0.794484</td>\n",
       "      <td>0.357576</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0.409807</td>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.358373</td>\n",
       "      <td>0.857729</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>0.383357</td>\n",
       "      <td>0.820285</td>\n",
       "      <td>0.350689</td>\n",
       "      <td>0.857488</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>0.397497</td>\n",
       "      <td>0.814947</td>\n",
       "      <td>0.426312</td>\n",
       "      <td>0.815700</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>0.407045</td>\n",
       "      <td>0.819395</td>\n",
       "      <td>0.437788</td>\n",
       "      <td>0.818116</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>0.380280</td>\n",
       "      <td>0.830071</td>\n",
       "      <td>0.323561</td>\n",
       "      <td>0.867874</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>3.753706</td>\n",
       "      <td>0.766904</td>\n",
       "      <td>2.689203</td>\n",
       "      <td>0.811594</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>0.505487</td>\n",
       "      <td>0.771352</td>\n",
       "      <td>0.429069</td>\n",
       "      <td>0.831643</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>0.402589</td>\n",
       "      <td>0.817616</td>\n",
       "      <td>0.383408</td>\n",
       "      <td>0.837198</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>0.388061</td>\n",
       "      <td>0.830961</td>\n",
       "      <td>0.359345</td>\n",
       "      <td>0.850483</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>0.384543</td>\n",
       "      <td>0.830071</td>\n",
       "      <td>0.383540</td>\n",
       "      <td>0.846377</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>0.421480</td>\n",
       "      <td>0.782918</td>\n",
       "      <td>0.433078</td>\n",
       "      <td>0.815700</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>0.412478</td>\n",
       "      <td>0.822954</td>\n",
       "      <td>0.392114</td>\n",
       "      <td>0.848792</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>768</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "0              3  0.412353  0.815836  0.366472  0.852174          False   \n",
       "1              3  0.457777  0.781139  0.449915  0.801691          False   \n",
       "2              3  0.419918  0.784698  0.399365  0.820048          False   \n",
       "3              3  0.488341  0.775801  0.469698  0.792512          False   \n",
       "4              3  0.457858  0.798932  0.383433  0.842029          False   \n",
       "5              3  0.415371  0.774911  0.412567  0.816908          False   \n",
       "6              3  0.408569  0.803381  0.395928  0.830435          False   \n",
       "7              3  0.428544  0.813167  0.375277  0.851208          False   \n",
       "8              3  0.419095  0.814057  0.373091  0.849758          False   \n",
       "9              3  0.379411  0.827402  0.356345  0.853382          False   \n",
       "10             3  3.757065  0.766904  2.240332  0.826329          False   \n",
       "11             3  3.757065  0.766904  2.953093  0.807971          False   \n",
       "12             3  3.757065  0.766904  2.957779  0.807971          False   \n",
       "13             3  0.408706  0.815836  0.407310  0.824155          False   \n",
       "14             3  3.757065  0.766904  2.925491  0.807971          False   \n",
       "15             3  3.754519  0.766904  1.678414  0.810628          False   \n",
       "16             3  0.399836  0.824733  0.423743  0.827053          False   \n",
       "17             3  0.463528  0.794484  0.357576  0.850000          False   \n",
       "18             3  0.409807  0.814947  0.358373  0.857729          False   \n",
       "19             3  0.383357  0.820285  0.350689  0.857488          False   \n",
       "20             3  0.397497  0.814947  0.426312  0.815700          False   \n",
       "21             3  0.407045  0.819395  0.437788  0.818116          False   \n",
       "22             3  0.380280  0.830071  0.323561  0.867874          False   \n",
       "23             3  3.753706  0.766904  2.689203  0.811594          False   \n",
       "24             3  0.505487  0.771352  0.429069  0.831643          False   \n",
       "25             3  0.402589  0.817616  0.383408  0.837198          False   \n",
       "26             3  0.388061  0.830961  0.359345  0.850483          False   \n",
       "27             3  0.384543  0.830071  0.383540  0.846377          False   \n",
       "28             3  0.421480  0.782918  0.433078  0.815700          False   \n",
       "29             3  0.412478  0.822954  0.392114  0.848792          False   \n",
       "\n",
       "    text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "0                      0                  400      0.3         32   \n",
       "1                      0                  400      0.5         32   \n",
       "2                      0                  400      0.4         32   \n",
       "3                      1                  400      0.5         32   \n",
       "4                      0                  768      0.3         32   \n",
       "5                      1                  400      0.5         32   \n",
       "6                      0                  768      0.5         32   \n",
       "7                      0                  768      0.4         32   \n",
       "8                      0                  768      0.3         32   \n",
       "9                      0                  768      0.4         32   \n",
       "10                     0                  768      0.3         32   \n",
       "11                     0                  400      0.5         32   \n",
       "12                     0                  768      0.3         32   \n",
       "13                     0                  400      0.5         32   \n",
       "14                     0                  400      0.4         32   \n",
       "15                     0                  400      0.5         32   \n",
       "16                     1                  400      0.4         32   \n",
       "17                     0                  400      0.3         32   \n",
       "18                     0                  400      0.3         32   \n",
       "19                     0                  400      0.3         32   \n",
       "20                     1                  400      0.5         32   \n",
       "21                     0                  768      0.5         32   \n",
       "22                     0                  768      0.4         32   \n",
       "23                     0                  768      0.4         32   \n",
       "24                     1                  768      0.5         32   \n",
       "25                     0                  768      0.4         32   \n",
       "26                     0                  400      0.5         32   \n",
       "27                     1                  400      0.3         32   \n",
       "28                     1                  768      0.5         32   \n",
       "29                     0                  768      0.3         32   \n",
       "\n",
       "    vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "0                     1                4096                      0   \n",
       "1                     1                2742                      1   \n",
       "2                     1                2742                      1   \n",
       "3                     0                4096                      1   \n",
       "4                     1                4096                      1   \n",
       "5                     1                2742                      0   \n",
       "6                     1                2742                      1   \n",
       "7                     0                4096                      0   \n",
       "8                     0                4096                      1   \n",
       "9                     0                2742                      0   \n",
       "10                    1                4096                      0   \n",
       "11                    1                2742                      0   \n",
       "12                    1                4096                      0   \n",
       "13                    0                4096                      1   \n",
       "14                    1                4096                      0   \n",
       "15                    1                4096                      0   \n",
       "16                    1                4096                      1   \n",
       "17                    0                2742                      0   \n",
       "18                    0                4096                      0   \n",
       "19                    1                2742                      1   \n",
       "20                    1                4096                      1   \n",
       "21                    1                2742                      1   \n",
       "22                    1                2742                      0   \n",
       "23                    1                4096                      0   \n",
       "24                    0                2742                      0   \n",
       "25                    0                2742                      1   \n",
       "26                    0                4096                      0   \n",
       "27                    1                4096                      1   \n",
       "28                    1                2742                      0   \n",
       "29                    1                2742                      1   \n",
       "\n",
       "    final_hidden_neurons optimizer  batch_size  epochs  \n",
       "0                     35      adam         256       3  \n",
       "1                     35   rmsprop         256       3  \n",
       "2                     35      adam         256       3  \n",
       "3                     35   rmsprop         256       3  \n",
       "4                     64      adam         256       3  \n",
       "5                     64      adam         256       3  \n",
       "6                     64      adam         256       3  \n",
       "7                     64      adam         256       3  \n",
       "8                     64   rmsprop         256       3  \n",
       "9                     35   rmsprop         256       3  \n",
       "10                    35      adam         256       3  \n",
       "11                    35      adam         256       3  \n",
       "12                    64   rmsprop         256       3  \n",
       "13                    64      adam         256       3  \n",
       "14                    64   rmsprop         256       3  \n",
       "15                    64      adam         256       3  \n",
       "16                    64      adam         256       3  \n",
       "17                    35   rmsprop         256       3  \n",
       "18                    64   rmsprop         256       3  \n",
       "19                    64      adam         256       3  \n",
       "20                    64      adam         256       3  \n",
       "21                    35   rmsprop         256       3  \n",
       "22                    35      adam         256       3  \n",
       "23                    35   rmsprop         256       3  \n",
       "24                    35   rmsprop         256       3  \n",
       "25                    35   rmsprop         256       3  \n",
       "26                    35   rmsprop         256       3  \n",
       "27                    35   rmsprop         256       3  \n",
       "28                    35      adam         256       3  \n",
       "29                    64      adam         256       3  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter experiment no 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'bert_trainable' :[False],\n",
    "    'text_no_hidden_layer':(0,2,2),\n",
    "    'text_hidden_neurons':[768,400],\n",
    "    'dropout':(0.3,0.6,3),\n",
    "    'repr_size':[32],\n",
    "    'vis_no_hidden_layer':(0,2,2),\n",
    "    'vis_hidden_neurons':[4096,2742],\n",
    "    'final_no_hidden_layer':(0,2,2),\n",
    "    'final_hidden_neurons':[64,35],\n",
    "    'optimizer':['adam','rmsprop'],\n",
    "    'batch_size':[256],\n",
    "    'epochs':[10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [2:50:04<00:00, 326.72s/it]\n"
     ]
    }
   ],
   "source": [
    "h = ta.Scan([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, params=params,\n",
    "            model=news_model,\n",
    "            dataset_name='chiness_fake_news',\n",
    "            experiment_no='2',\n",
    "            x_val=[test_input_ids, test_input_masks, test_segment_ids,test_imagesX],\n",
    "            y_val=testY,\n",
    "            grid_downsample=.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ta.Reporting(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8674377200866509"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.high('val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10</td>\n",
       "      <td>0.319719</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.265550</td>\n",
       "      <td>0.890821</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10</td>\n",
       "      <td>0.316719</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.253544</td>\n",
       "      <td>0.892754</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.315661</td>\n",
       "      <td>0.862100</td>\n",
       "      <td>0.237169</td>\n",
       "      <td>0.904831</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>0.313762</td>\n",
       "      <td>0.862100</td>\n",
       "      <td>0.280370</td>\n",
       "      <td>0.880435</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10</td>\n",
       "      <td>0.308678</td>\n",
       "      <td>0.867438</td>\n",
       "      <td>0.240164</td>\n",
       "      <td>0.900725</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "27            10  0.319719  0.859431  0.265550  0.890821          False   \n",
       "24            10  0.316719  0.859431  0.253544  0.892754          False   \n",
       "3             10  0.315661  0.862100  0.237169  0.904831          False   \n",
       "20            10  0.313762  0.862100  0.280370  0.880435          False   \n",
       "16            10  0.308678  0.867438  0.240164  0.900725          False   \n",
       "\n",
       "    text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "27                     1                  768      0.4         32   \n",
       "24                     1                  400      0.5         32   \n",
       "3                      1                  400      0.3         32   \n",
       "20                     1                  768      0.4         32   \n",
       "16                     1                  400      0.3         32   \n",
       "\n",
       "    vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "27                    0                4096                      0   \n",
       "24                    1                4096                      0   \n",
       "3                     0                2742                      0   \n",
       "20                    0                2742                      1   \n",
       "16                    0                2742                      1   \n",
       "\n",
       "    final_hidden_neurons optimizer  batch_size  epochs  \n",
       "27                    64      adam         256      10  \n",
       "24                    35      adam         256      10  \n",
       "3                     35      adam         256      10  \n",
       "20                    35      adam         256      10  \n",
       "16                    64      adam         256      10  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data.sort_values('val_acc').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x756 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert_trainable\n",
      "text_no_hidden_layer\n",
      "text_hidden_neurons\n",
      "dropout\n",
      "repr_size\n",
      "vis_no_hidden_layer\n",
      "vis_hidden_neurons\n",
      "final_no_hidden_layer\n",
      "final_hidden_neurons\n",
      "optimizer\n",
      "batch_size\n",
      "epochs\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in params.keys():\n",
    "    print(k)\n",
    "    r.plot_box(y='val_acc',x=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c840a40f0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = r.data[['text_no_hidden_layer','final_no_hidden_layer','optimizer']]\n",
    "scaler = MinMaxScaler()\n",
    "y = scaler.fit_transform(r.data[['val_acc']])\n",
    "X['optimizer'] = pd.factorize(X['optimizer'])[0]\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "reg = RandomForestRegressor(max_depth=3,n_estimators=100)\n",
    "reg.fit(X,y)\n",
    "pd.Series(reg.feature_importances_,index=X.columns).\\\n",
    "sort_values(ascending=True).plot.barh(color='grey',title='Feature Importance of Hyperparameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_regs(x='acc',y='val_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x475.2 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot_kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using LR finder to find best learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_epochs</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>loss</th>\n",
       "      <th>acc</th>\n",
       "      <th>bert_trainable</th>\n",
       "      <th>text_no_hidden_layer</th>\n",
       "      <th>text_hidden_neurons</th>\n",
       "      <th>dropout</th>\n",
       "      <th>repr_size</th>\n",
       "      <th>vis_no_hidden_layer</th>\n",
       "      <th>vis_hidden_neurons</th>\n",
       "      <th>final_no_hidden_layer</th>\n",
       "      <th>final_hidden_neurons</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>epochs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10</td>\n",
       "      <td>0.319719</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.265550</td>\n",
       "      <td>0.890821</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10</td>\n",
       "      <td>0.316719</td>\n",
       "      <td>0.859431</td>\n",
       "      <td>0.253544</td>\n",
       "      <td>0.892754</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4096</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.315661</td>\n",
       "      <td>0.862100</td>\n",
       "      <td>0.237169</td>\n",
       "      <td>0.904831</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>0.313762</td>\n",
       "      <td>0.862100</td>\n",
       "      <td>0.280370</td>\n",
       "      <td>0.880435</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10</td>\n",
       "      <td>0.308678</td>\n",
       "      <td>0.867438</td>\n",
       "      <td>0.240164</td>\n",
       "      <td>0.900725</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>0.3</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>2742</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>256</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    round_epochs  val_loss   val_acc      loss       acc bert_trainable  \\\n",
       "27            10  0.319719  0.859431  0.265550  0.890821          False   \n",
       "24            10  0.316719  0.859431  0.253544  0.892754          False   \n",
       "3             10  0.315661  0.862100  0.237169  0.904831          False   \n",
       "20            10  0.313762  0.862100  0.280370  0.880435          False   \n",
       "16            10  0.308678  0.867438  0.240164  0.900725          False   \n",
       "\n",
       "    text_no_hidden_layer  text_hidden_neurons  dropout  repr_size  \\\n",
       "27                     1                  768      0.4         32   \n",
       "24                     1                  400      0.5         32   \n",
       "3                      1                  400      0.3         32   \n",
       "20                     1                  768      0.4         32   \n",
       "16                     1                  400      0.3         32   \n",
       "\n",
       "    vis_no_hidden_layer  vis_hidden_neurons  final_no_hidden_layer  \\\n",
       "27                    0                4096                      0   \n",
       "24                    1                4096                      0   \n",
       "3                     0                2742                      0   \n",
       "20                    0                2742                      1   \n",
       "16                    0                2742                      1   \n",
       "\n",
       "    final_hidden_neurons optimizer  batch_size  epochs  \n",
       "27                    64      adam         256      10  \n",
       "24                    35      adam         256      10  \n",
       "3                     35      adam         256      10  \n",
       "20                    35      adam         256      10  \n",
       "16                    64      adam         256      10  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.data.sort_values('val_acc').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_final = {\n",
    "    'bert_trainable' :False,\n",
    "    'text_no_hidden_layer':1,\n",
    "    'text_hidden_neurons':400,\n",
    "    'dropout':0.3,\n",
    "    'repr_size':32,\n",
    "    'vis_no_hidden_layer':0,\n",
    "    'vis_hidden_neurons':2742,\n",
    "    'final_no_hidden_layer':1,\n",
    "    'final_hidden_neurons':64,\n",
    "    'optimizer':tf.keras.optimizers.Adam\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=get_news_model(params_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.set_value(model.optimizer.lr, 0.0005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = tf.keras.callbacks.ModelCheckpoint('model-{epoch:03d}-{val_acc:03f}.h5', verbose=1, monitor='val_acc',save_best_only=True, mode='max')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00050: val_acc did not improve from 0.89235\n"
     ]
    }
   ],
   "source": [
    "out = model.fit([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY,\n",
    "                    batch_size=256,\n",
    "                    epochs=50,\n",
    "                    verbose=0,\n",
    "                    shuffle=True,\n",
    "                    validation_data=([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY),callbacks=[live(),checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=get_news_model(params_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('model-042-0.892349.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predict = model.predict([test_input_ids, test_input_masks, test_segment_ids,test_imagesX])\n",
    "test_predict = [1 if i>=0.5 else 0 for i in test_predict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy == 0.8923487544483986\n",
      "F1 == [0.93213685 0.73978495]\n",
      "Precision == [0.90228013 0.84729064]\n",
      "Recall == [0.96403712 0.65648855]\n"
     ]
    }
   ],
   "source": [
    "print(f'Accuracy == {accuracy_score(testY,test_predict)}')\n",
    "print(f'F1 == {f1_score(testY,test_predict,average=None)}')\n",
    "print(f'Precision == {precision_score(testY,test_predict,average=None)}')\n",
    "print(f'Recall == {recall_score(testY,test_predict,average=None)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = dict()\n",
    "# for i in tqdm(range(20)):\n",
    "#     p = dict()\n",
    "#     for k,v in params.items():\n",
    "#         p[k] = choice(v)\n",
    "        \n",
    "#     history,model = news_model([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, [test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY,p)\n",
    "#     val_loss, val_acc = model.evaluate([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY,verbose=0)\n",
    "# #     train_loss,train_acc = model.evaluate([train_input_ids, train_input_masks, train_segment_ids,train_imagesX],trainY)\n",
    "  \n",
    "#     results[i] = {'p':p,'val_loss':val_loss,'val_acc':val_acc,'history':history}\n",
    "# start=0.001\n",
    "# end=0.01\n",
    "# n=4\n",
    "# np.arange(start, end, (end - start) / n, dtype=float)/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder = LRFinder(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.find([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, 0.0000001, 1, 512, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_finder.plot_loss_change(sma=20, n_skip_beginning=20, n_skip_end=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K.set_value(model.optimizer.lr, 0.00005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# history = model.fit([train_input_ids, train_input_masks, train_segment_ids,train_imagesX], trainY, validation_data=([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY),shuffle= True, epochs=10, batch_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(history.history['acc'])\n",
    "# plt.plot(history.history['val_acc'])\n",
    "# plt.title('model accuracy')\n",
    "# plt.ylabel('accuracy')\n",
    "# plt.xlabel('epoch')\n",
    "# plt.axhline(y=0.76,linewidth=1, color='r')\n",
    "# plt.legend(['train', 'val'], loc='upper left')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(history.history['loss'])\n",
    "# plt.plot(history.history['val_loss'])\n",
    "# plt.title('model loss')\n",
    "# plt.ylabel('loss')\n",
    "# plt.xlabel('epoch')\n",
    "# # plt.ylim((0,1))\n",
    "# plt.legend(['train', 'val'], loc='upper left')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.evaluate([test_input_ids, test_input_masks, test_segment_ids,test_imagesX],testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.evaluate([train_input_ids, train_input_masks, train_segment_ids,train_imagesX],trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (fake_news)",
   "language": "python",
   "name": "fake_news"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
